Sample records for spatio-temporal blind source

The electromagnetic brain activity measured via MEG (or EEG) can be interpreted as arising from a collection of current dipoles or sources located throughout the cortex. Because the number of candidate locations for these sources is much larger than the number of sensors, source reconstruction involves solving an inverse problem that is severely underdetermined. Bayesian graphical models provide a powerful means of incorporating prior assumptions that narrow the solution space and lead to tractable posterior distributions over the unknown sources given the observed data. In particular, this paper develops a hierarchical, spatio-temporal Bayesian model that accommodates the principled computation of sparse spatial and smooth temporal M/EEG source reconstructions consistent with neurophysiological assumptions in a variety of event-related imaging paradigms. The underlying methodology relies on the notion of automatic relevance determination (ARD) to express the unknown sources via a small collection of spatio-temporal basis functions. Experiments with several data sets provide evidence that the proposed model leads to improved source estimates. The underlying methodology is also well-suited for estimation problems that arise from other brain imaging modalities such as functional or diffusion weighted MRI.

We have applied the eigenspace-based beamformer to reconstruct spatio-temporal activities of neural sources from MEG data. The weight vector of the eigenspace-based beamformer is obtained by projecting the weight vector of the minimum-variance beamformer onto the signal subspace of a measurement covariance matrix. This projection removes the residual noise-subspace component that considerably degrades the signal-to-noise ratio (SNR) of the beamformer output when errors in estimating the sensor lead field exist. Therefore, the eigenspace-based beamformer produces a SNR considerably higher than that of the minimum-variance beamformer in practical situations. The effectiveness of the eigenspace-based beamformer was validated in our numerical experiments and experiments using auditory responses. We further extended the eigenspace-based beamformer so that it incorporates the information regarding the noise covariance matrix. Such a prewhitened eigenspace beamformer was experimentally demonstrated to be useful when large background activity exists. PMID:11835609

Microwave sheath-Voltage combination Plasma (MVP) is a high density plasma source and can be used as a suitable plasma processing device (e.g., ionized physical vapor deposition). In the present report, the spatio-temporal behavior of an argon MVP sustained along a direct-current biased Ti rod is investigated. Two plasma modes are observed, one is an "oxidized state" (OS) at the early time of the microwave plasma and the other is "ionized sputter state" (ISS) at the later times. Transition of the plasma from OS to ISS results a prominent change in the visible color of the plasma, resulting from a significant increase in the plasma density, as measured by a Langmuir probe. In the OS, plasma is dominated by Ar ions, and the density is in amplitude order of 1011 cm-3. In the ISS, metal ions from the Ti rod contribute significantly to the ion composition, and higher density plasma (1012 cm-3) is produced. Nearly uniform high density plasma along the length of the Ti rod is produced at very low input microwave powers (around 30 W). Optical emission spectroscopy measurements confirm the presence of sputtered Ti ions and Ti neutrals in the ISS.

Discusses constant modulus signal recovery by a multi-sensor receiver in a multipath propagation channel. The author first shows that spatio-temporal filtering can recover the transmitted signal provided that the number of sensors minus one times the length of the temporal filtering be larger than the intersymbol interference length assumed to be finite. It is also shown that spatio-temporal filtering is

A new method is proposed for blind identification of possibly nonminimum phase FIR channels with multiple users. The technique exploits the structure of the signals received by an antenna array in both the temporal and spatial frequency domains. Although in the single antenna case it is necessary to use cyclostationary signals or higher order statistics to identify the magnitude and

Knowledge harvesting enables the automated construction of large knowledge bases. In this work, we made a first attempt to harvest spatio-temporal knowledge from news archives to construct trajectories of individual entities for spatio-temporal entity tracking. Our approach consists of an entity extraction and disambiguation module and a fact generation module which produce pertinent trajectory records from textual sources. The evaluation

The June 17, 2007 intrusion in the upper East Rift Zone (ERZ) of Kilauea volcano, Hawaii was particularly well monitored with continuous GPS, tilt, and InSAR. The first indication of activity was increased seismicity from Kilauea's summit to the bend in the ERZ. The upper ERZ tilted ~70 ? rad sharply down to the south from 02:16 to 07:40 HST, and within minutes the summit crossing GPS baseline began to shorten and eventually decreased by ~14 cm over 2.5 days. Beginning between 08:00 and 0:900, the rift-spanning GPS baseline down rift lengthened by ~1 meter in just over two days. In the two following days, surface cracks were observed in the upper ERZ, and a small amount of new lava was seen on the northeast side of Kane Nui o Hamo. We test distributed source models with a variety of dike geometries that follow observed surface cracks, the axis of InSAR fringes, and the optimal uniform opening model (Sinnett et al., this volume). The sources included are the ERZ dike, the south flank decollement, and the summit magma reservoir. The dike and decollement are modeled as distributed rectangular dislocations, and the summit magma reservoir with a Mogi source. While several models explain most of the data, none fits the details of the data near the western end of the dike, perhaps indicating a complex source geometry, inelastic deformation, or differing mechanisms of pre-, co- and post- intrusion. In the preferred model, following the axis of deformation observed by InSAR, the maximum opening of 2.33 m is between 0-2 km deep under Kane Nui o Hamo, just north of Makaopui crater. Another opening maximum of nearly 2 m occurs about 2 km deep between Pauahi and Mauna Ulu. Seismicity during the intrusion was concentrated below the opening maxima, with some events between them. Unlike previous ERZ intrusions (e.g. Jan. 1997, Owen et al., GRL, 2000) the dike opening does not account for all of the deformation observed at the GPS sites on the southwest flank, nor does it agree with the tilt direction at Kaena Point, indicating possible decollement slip during the intrusion (B. Brooks, pers. comm.). Inversions including a decollement favor slip of up to 30 cm and improve data fits locally, especially in the western part of the network, although the overall amount of data variance explained (~ %70) is similar to the dike-only models. We use the Kalman filter-based Extended Network Inversion Filter (McGuire and Segall, GJI, 2003) to invert kinematic GPS solutions that have been smoothed to reduce multipath (Larson et al., JGR, 2001) and sampled every 4 minutes, and tilt, sampled every 1 minute, for the spatio-temporal slip evolution. The source parameters are the same as the preferred distributed model. Because the dike is short relative to station spacing, lateral resolution of propagation is limited, however, refined models my be able to determine the relative timing of the intrusion and slow slip.

For M/EEG-based distributed source imaging, it has been established that the L2-norm-based methods are effective in imaging spatially extended sources, whereas the L1-norm-based methods are more suited for estimating focal and sparse sources. However, when the spatial extents of the sources are unknown a priori, the rationale for using either type of methods is not adequately supported. Bayesian inference by exploiting the spatio-temporal information of the patch sources holds great promise as a tool for adaptive source imaging, but both computational and methodological limitations remain to be overcome. In this paper, based on state-space modeling of the M/EEG data, we propose a fully data-driven and scalable algorithm, termed STRAPS, for M/EEG patch source imaging on high-resolution cortices. Unlike the existing algorithms, the recursive penalized least squares (RPLS) procedure is employed to efficiently estimate the source activities as opposed to the computationally demanding Kalman filtering/smoothing. Furthermore, the coefficients of the multivariate autoregressive (MVAR) model characterizing the spatial-temporal dynamics of the patch sources are estimated in a principled manner via empirical Bayes. Extensive numerical experiments demonstrate STRAPS's excellent performance in the estimation of locations, spatial extents and amplitudes of the patch sources with varying spatial extents. PMID:25903226

Multichannel aspect allows the introduction of blind channel estimation techniques. Most existing such techniques for frequency-selective channels are quite complex. In this paper, we consider the blind channel estimation problem for Single Input Multi Output (SIMO) cyclic prefix (CP) systems. We have shown before that blind channel estimation becomes computationally much more attractive and more straight forward to analyze in

Identifying the temp-spatial distribution and sources of water pollutants is of great significance for efficient water quality management pollution control in Wenruitang River watershed, China. A total of twelve water quality parameters, including temperature, pH, dissolved oxygen (DO), total nitrogen (TN), ammonia nitrogen (NH4+ -N), electrical conductivity (EC), turbidity (Turb), nitrite-N (NO2-), nitrate-N(NO3-), phosphate-P(PO4(3-), total organic carbon (TOC) and silicate (SiO3(2-)), were analyzed from September, 2008 to October, 2009. Geographic information system(GIS) and principal component analysis(PCA) were used to determine the spatial distribution and to apportion the sources of pollutants. The results demonstrated that TN, NH4+ -N, PO4(3-) were the main pollutants during flow period, wet period, dry period, respectively, which was mainly caused by urban point sources and agricultural and rural non-point sources. In spatial terms, the order of pollution was tertiary river > secondary river > primary river, while the water quality was worse in city zones than in the suburb and wetland zone regardless of the river classification. In temporal terms, the order of pollution was dry period > wet period > flow period. Population density, land use type and water transfer affected the water quality in Wenruitang River. PMID:25898648

Modern mapping technologies used for coastal studies such as LIDAR and RTK-GPS produce massive amounts of data characterized by oversampling and noise. The physical phenomena and landscape changes examined are often subtle and besides statistical accuracy, adequate representation of surface geometry is crucial for correct interpretation of measured data. We have explored the suitability of the Open source GRASS GIS

Characterizing the spatio-temporal patterns and apportioning the pollution sources of water bodies are important for the management and protection of water resources. The main objective of this study is to describe the dynamics of water quality and provide references for improving river pollution control practices. Comprehensive application of neural-based modeling and different multivariate methods was used to evaluate the spatio-temporal patterns and source apportionment of pollution in Qiantang River, China. Measurement data were obtained and pretreated for 13 variables from 41 monitoring sites for the period of 2001-2004. A self-organizing map classified the 41 monitoring sites into three groups (Group A, B and C), representing different pollution characteristics. Four significant parameters (dissolved oxygen, biochemical oxygen demand, total phosphorus and total lead) were identified by discriminant analysis for distinguishing variations of different years, with about 80% correct assignment for temporal variation. Rotated principal component analysis (PCA) identified four potential pollution sources for Group A (domestic sewage and agricultural pollution, industrial wastewater pollution, mineral weathering, vehicle exhaust and sand mining), five for Group B (heavy metal pollution, agricultural runoff, vehicle exhaust and sand mining, mineral weathering, chemical plants discharge) and another five for Group C (vehicle exhaust and sand mining, chemical plants discharge, soil weathering, biochemical pollution, mineral weathering). The identified potential pollution sources explained 75.6% of the total variances for Group A, 75.0% for Group B and 80.0% for Group C, respectively. Receptor-based source apportionment was applied to further estimate source contributions for each pollution variable in the three groups, which facilitated and supported the PCA results. These results could assist managers to develop optimal strategies and determine priorities for river pollution control and effective water resources management.

complex geospatial objects. MapServer is a flexible and easy tool for serving data on the WebThe GLIMS Glacier Database: a spatio-temporal database implemented using Open Source tools Bruce small budgets). Capable, and fast! Runs on Linux, where we can take advantage of our stock of Linux

Nitrate contamination remains a ubiquitous groundwater pollution problem worldwide. Animal farming systems are among the major sources of groundwater nitrate. Little is known about the impact of dairy farming practices on water quality in the extensive alluvial aquifers underlying many animal farming regions in the United States and elsewhere. The objective of this work is to characterize and assess nitrate

Simultaneous EEG–fMRI acquisitions in patients with epilepsy often reveal distributed patterns of Blood Oxygen Level Dependant (BOLD) change correlated with epileptiform discharges. We investigated if electrical source imaging (ESI) performed on the interictal epileptiform discharges (IED) acquired during fMRI acquisition could be used to study the dynamics of the networks identified by the BOLD effect, thereby avoiding the limitations of

The current study considers the spatial and temporal variability in aerosol trace metal concentrations (Al, Fe, Mn, Cr, Cu, Pb, Cd, Zn) in the Levantine Basin of the Eastern Mediterranean, utilising an extensive sample library ( n=621) collected between 1999 and 2001, at two coastal sites located at the northern, Erdemli (Turkey) and southeastern, Tel Shikmona (TS, Israel), region of the Basin. A critical evaluation of the datasets from the two locations was presented. Enhanced concentrations of Al (1.7×), Fe (1.8×), Mn (2.1×) were detected at the more southerly sampling station, during common dust events, owing to the greater proximity of desert dust sources (NE Africa and Saudi Peninsula); leading to a gradual decline in crustal inputs northwards across the basin. An insignificant Pb gradient was noticed across the Levantine Basin, which has exhibited a decadal decrease (40%). Cr was enriched in the north by a factor of three accounted by local sources. Cu was also enriched, to a lower extent, by about a factor of two. Seasonal variations of the crustal elements (Al, Fe, Mn) at Erdemli were detected (transitional>summer>winter) owing to both, a greater frequency and intensity of dust events during the transitional period and a greater washout effect during winter. It is likely that similar variations occur at TS as both sites experienced similar dust and rainfall events. It was observed at Erdemli that all elements (except Pb and Cd) exhibit their lowest concentrations in the winter period due to a greater washout effect. The lack of seasonal difference between winter and summer for Pb and Cd may have been due to the relatively high emission intensities of regional sources rapidly regenerating aerosol concentrations and their association with fine particles which are less efficiently scavenged during rain events. During the summer, Zn derived from local transportation and agricultural activities, was more pronounced, leading to an enhancement of around 10% in its concentration.

SummaryReducing nitrate pollution from diffuse agricultural sources is the major environmental challenge in the two adjacent catchments of the Oja-Tirón and Zamaca rivers (La Rioja and Castilla y León, northern Spain). For this reason, part of their territory was designated a Nitrate Vulnerable Zone (NVZ) according to the Nitrates Directive. The Oja Alluvial Aquifer, the Tirón Alluvial Aquifer and their associated rivers are particularly vulnerable to nitrogen pollution due to the shallow water table, the high permeability of alluvial deposits, interconnections between the alluvial aquifers and surface waters and pressures from agriculture. To this end, nine sampling campaigns, organised on a semi-annual basis and focused on the rivers and alluvial aquifers of the two catchments, were carried out from April 2005 to April 2009. The main objectives of the study were: (1) to investigate the chemical forms of nitrogen in river-alluvial aquifer systems of the Oja-Tirón and Zamaca catchments, (2) to improve our understanding of the spatio-temporal patterns of nitrogen distribution in the alluvial aquifers and associated rivers by integrating hydrochemical data and hydrogeological and environmental parameters, (3) to estimate the amount of nitrogen exported from the rivers and alluvial aquifers to the River Ebro, and (4) to evaluate the suitability of the current method of designating NVZs in the area. High groundwater flow velocities in the upper alluvial zones favoured the advective transport of nitrate and generated a dilution effect. In these areas, inter-annual variations in nitrate concentrations were observed related to precipitation and N-input from agriculture. However, low flow velocities favoured processes of accumulation in the lower alluvial zones. Our results demonstrated that the entire alluvial surface was highly vulnerable, according to dynamics of the nitrogen in the river-alluvial aquifer systems being studied. The amount of nitrogen exported from these river-alluvial aquifer systems to the River Ebro was estimated at 2.4 ± 0.2 kt year -1. Findings from this investigation highlight the need to include the alluvial area corresponding to the Tirón aquifer as a NVZ, particularly as the Tirón sub-catchment provides more than half of the nitrogen exported from the River Tirón to the River Ebro. Based in these results, at least the entire alluvial surface in the study area should be considered a NVZ in order to address the recovery of water quality.

To navigate through daily life, humans use their ability to conceptualize spatio-temporal information, which ultimately leads to a system of categories. Likewise, the spatial sciences rely heavily on conceptualization and categorization as means to create knowledge when they process spatio-temporal data. In the spatial sciences and in related branches of artificial intelligence, an approach has been developed for processing spatio-temporal data on the level of coarse categories: qualitative spatio-temporal representation and reasoning (QSTR). Calculi developed in QSTR allow for the meaningful processing of and reasoning with spatio-temporal information. While qualitative calculi are widely acknowledged in the cognitive sciences, there is little behavioral assessment whether these calculi are indeed cognitively adequate. This is an astonishing conundrum given that these calculi are ubiquitous, are often intended to improve processes at the human-machine interface, and are on several occasions claimed to be cognitively adequate. We have systematically evaluated several approaches to formally characterize spatial relations from a cognitive-behavioral perspective for both static and dynamically changing spatial relations. This contribution will detail our framework, which is addressing the question how formal characterization of space can help us understand how people think with, in, and about space. PMID:22806649

Variations in spatio-temporal patterns of Human Monocytic Ehrlichiosis (HME) infection in the state of Kansas, USA were examined and the relationship between HME relative risk and various environmental, climatic and socio-economic variables were evaluated. HME data used in the study was reported to the Kansas Department of Health and Environment between years 2005-2012, and geospatial variables representing the physical environment [National Land cover/Land use, NASA Moderate Resolution Imaging Spectroradiometer (MODIS)], climate [NASA MODIS, Prediction of Worldwide Renewable Energy (POWER)], and socio-economic conditions (US Census Bureau) were derived from publicly available sources. Following univariate screening of candidate variables using logistic regressions, two Bayesian hierarchical models were fit; a partial spatio-temporal model with random effects and a spatio-temporal interaction term, and a second model that included additional covariate terms. The best fitting model revealed that spatio-temporal autocorrelation in Kansas increased steadily from 2005-2012, and identified poverty status, relative humidity, and an interactive factor, 'diurnal temperature range x mixed forest area' as significant county-level risk factors for HME. The identification of significant spatio-temporal pattern and new risk factors are important in the context of HME prevention, for future research in the areas of ecology and evolution of HME, and as well as climate change impacts on tick-borne diseases. PMID:24992684

Most audio signals are mixtures of several audio sources which are active simultaneously. For example, live debates are mixtures of several speakers, music CDs are mixtures of musical instruments and singers, and movie soundtracks are mixtures of speech, music and natural sounds. Blind Audio Source Separation (BASS) is the problem of recovering each source signal from a given mixture signal.

From a video object segmentation perspective, using a joint spatio-temporal strategy is superior to processing with priority in either the spatial or temporal domains, as it considers a video sequence as a spatio-temporal grouping of pixels. However, existing spatio-temporal object segmentation techniques consider only pixel features, which tend to limit their performance in being able to segment arbitrary shaped objects.

In recent years, spatio-temporal databases have been studied intensively. This paper proposes how to process k closest pair queries in spatio-temporal databases for the flrst time. A spatio-temporal k closest pair query continuously searches the k closest pairs between a set of spatial objects and a set of moving objects for a specifled time interval of the query. To maintain

Background Every day, around 400 million tweets are sent worldwide, which has become a rich source for detecting, monitoring and analysing news stories and special (disaster) events. Existing research within this field follows key words attributed to an event, monitoring temporal changes in word usage. However, this method requires prior knowledge of the event in order to know which words to follow, and does not guarantee that the words chosen will be the most appropriate to monitor. Methods This paper suggests an alternative methodology for event detection using space-time scan statistics (STSS). This technique looks for clusters within the dataset across both space and time, regardless of tweet content. It is expected that clusters of tweets will emerge during spatio-temporally relevant events, as people will tweet more than expected in order to describe the event and spread information. The special event used as a case study is the 2013 London helicopter crash. Results and Conclusion A spatio-temporally significant cluster is found relating to the London helicopter crash. Although the cluster only remains significant for a relatively short time, it is rich in information, such as important key words and photographs. The method also detects other special events such as football matches, as well as train and flight delays from Twitter data. These findings demonstrate that STSS is an effective approach to analysing Twitter data for event detection. PMID:24893168

The spatio-temporal correlation functions of pressure of the thermal acoustic radiation are measured for immovable heated sources: narrow plasticine plate, two narrow parallel plasticine plates and broad plasticine plate. The correlation dependencies are obtained with multiplication of signals (measured with two receivers) shifted with time delay. The functions oscillate (the oscillation period is defined by mean reception frequency, amplitude envelope

. To allow modeling patterns of spatio- temporal dynamics, in particular, the flow of oxygenated blood, we to likely reflect flow of oxygenated blood in V1. 1 Introduction The blood oxygenation level dependent (BOLD models are a way to account for dynamic flow patterns. In convolutive models, each source process

Web is entering a new phase - HTML5. New features of HTML5 should be studied for online spatio-temporal data visualization. In the proposed framework, spatio-temporal data is stored in the data server and is sent to user browsers with WebSocket. Public geo-data such as Internet digital map is integrated into the browsers. Then animation is implemented through the canvas object defined by the HTML5 specification. To simulate the spatio-temporal data source, we collected the daily location of 15 users with GPS tracker. The current positions of the users are collected every minute and are recorded in a file. Based on this file, we generate a real time spatio-temporal data source which sends out current user location every second.By enlarging the real time scales by 60 times, we can observe the movement clearly. The data transmitted with WebSocket is the coordinates of users' current positions, which will can be demonstrated in client browsers.

Spatio-Temporal and Context Reasoning in Smart Homes Sook-Ling Chua, Stephen Marsland, and Hans W intelligence has a wide range of applications, includ- ing smart homes. Smart homes can support. In this paper, we discuss spatio-temporal and context-based reasoning in smart homes and some methods by which

We propose a pattern matching language for spatio-temporal databases. The matching process in time dimension is based upon the evolutionary nature of time, but in spatial dimension it is based on placement, shape and sizes of regions. The concept of pattern matching introduced in this paper is independent of the choice of the underlying model for spatio-temporal databases. In particular,

Spatio-temporal modelling of corrosion in an industrial furnace John Little, Michael Goldstein-scale industrial furnace subject to corrosion will be considered. A suitable Bayesian spatio-temporal dynamic of paper In Section 2, we introduce the motivating example - an industrial furnace used in the oil refining

Missing values occur frequently in many different statistical applications and need to be dealt with carefully, especially when the data are collected spatio-temporally. We propose a method called CUTOFF imputation that utilizes the spatio-temporal nature of the data to accurately and efficiently impute missing values. The main feature of this method is that the estimate of a missing value is produced by incorporating similar observed temporal information from the value's nearest spatial neighbors. Extensions to this method are also developed to expand the method's ability to accommodate other data generating processes. We develop a cross-validation procedure that optimally chooses parameters for CUTOFF, which can be used by other imputation methods as well. We analyze some rainfall data from 78 gauging stations in the Murray-Darling Basin in Australia using the CUTOFF imputation method and compare its performance to four well-studied competing imputation methods, namely, k-nearest neighbors, singular value decomposition, multiple imputation and random forest. Empirical results show that our method captures the temporal patterns well and is effective at imputing large gaps in the data. Compared to the competing methods, CUTOFF is more accurate and much faster. We analyze further examples to demonstrate CUTOFF's applications to two different data sets and provide extra evidence of its validity and usefulness. We implement a simulation study based on the Murray-Darling Basin data to evaluate the method; the results show that our method performs well in both accuracy and computational efficiency.

Potential trophic competition between two sympatric mullet species, Mugil cephalus and Mugil curema, was explored in the hypersaline estuary of the Saloum Delta (Senegal) using ?(13) C and ?(15) N composition of muscle tissues. Between species, ?(15) N compositions were similar, suggesting a similar trophic level, while the difference in ?(13) C compositions indicated that these species did not feed from exactly the same basal production sources or at least not in the same proportions. This result provides the first evidence of isotopic niche segregation between two limno-benthophageous species belonging to the geographically widespread, and often locally abundant, Mugilidae family. PMID:25846862

Atmosphere is regarded to be an important media in the environmental pollution research area. Passive air sampling was one of the effective complementary sampling techniques for the active high volume air sampler in recent decades. A regional scale investigation on the atmospheric polycyclic aromatic hydrocarbons (PAHs) was conducted in the Yangtze River Delta (YRD). Polyurethane foam based passive air samplers were used to collect the atmospheric PAHs from 31 sampling sites in this area. PAHs concentrations ranged from 10.1 ng x m(-1) to 367 ng x m(-3) in this study. The annual average concentration of benzo [a] pyrene (BaP) reached 2.25 ng x m(-3), which was two times higher exceeding the national standard, GB 3095-2012. The atmospheric PAHs during four seasons decreased in the following order: autumn > winter > spring > summer. Larger BaP excessive areas were found in autumn and winter than other seasons. Moreover, an obvious emission of BaP was confirmed during the winter time. Traffic related petroleum combustion, coal and biomass burning, and coke oven were identified as potential sources of atmospheric PAHs, contributing 38.1%, 42.4%, and 19.5%, respectively. PMID:24288973

One of the water source areas of the South-to-North Water Diversion Project is the Danjiangkou Reservoir (DJKR). To understand seasonal variation in phytoplankton composition, abundance and distribution in the DJKR area before water diversion, as well as to estimate potential risks of water quality after water diversion, we conducted an investigation on phytoplankton in the DJKR from August 2008 to May 2009. The investigation included 10 sampling sites, each with four depths of 0.5, 5, 10, and 20 m. In this study, 117 taxa belonging to 76 genera were identified, consisting of diatoms (39 taxa), green algae (47 taxa), blue-green algae (19 taxa), and others (12 taxa). Annual average phytoplankton abundance was 2.01 × 106 ind./L, and the highest value was 14.72 × 106 ind/L (at site 3 in August 2008). Phytoplankton abundance in front of the Danjiangkou Dam (DJKD) was higher than that of the Danjiang Reservoir Basin. Phytoplankton distribution showed a vertical declining trend from 0.5 m to 20 m at most sites in August 2008 (especially at sites of 1, 2, 4 and 10), but no distinct pattern in other sampling months. In December 2008 and March 2009, Stephanodiscus sp. was the most abundant species, amounting to 55.23% and 72.34%, respectively. We propose that high abundance of Stephanodiscus sp. may have contributed greatly to the frequent occurrence of Stephanodiscus sp. blooms in middle-low reaches of the Hanjiang River during the early spring of 2009. In comparison with previous studies conducted from 1992 to 2006, annual average phytoplankton density, green algae and blue-green algae species, as well as major nutrient concentrations increased, while phytoplankton diversity indices declined. This indicates a gradual decline in water quality. More research should be conducted and countermeasures taken to prevent further deterioration of water quality in the DJKR.

This dissertation describes spatio-temporal properties of rainfall. Rainfall in space was modeled by a precipitation areal reduction factor (ARF) using a NEXRAD image. The storms are represented as ellipses, which are determined by maximizing...

Creating an effective environment for collaborative spatio-temporal model development will require computational systems that provide support for the user in three key areas: (1) Support for modular, hierarchical model construction and archiving/linking of simulation modules; (2)...

The progress of the integrated control policy for schistosomiasis implemented since 2005 in China, which is aiming at reducing the roles of bovines and humans as infection sources, may be challenged by persistent presence of infected snails in lake and marshland areas. Based on annual parasitologic data for schistosomiasis during 2004-2011 in Xingzi County, a spatio-temporal kriging model was used to investigate the spatio-temporal pattern of schistosomiasis risk. Results showed that environmental factors related to snail habitats can explain the spatio-temporal variation of schistosomiasis. Predictive maps of schistosomiasis risk illustrated that clusters of the disease fluctuated during 2004-2008; there was an extensive outbreak in 2008 and attenuated disease occurrences afterwards. An area with an annually constant cluster of schistosomiasis was identified. Our study suggests that targeting snail habitats located within high-risk areas for schistosomiasis would be an economic and sustainable way of schistosomiasis control in the future. PMID:24980498

We propose a novel ?1?2-norm inverse solver for estimating the sources of EEG/MEG signals. Developed based on the standard ?1-norm inverse solvers, this sparse distributed inverse solver integrates the ?1-norm spatial model with a temporal model of the source signals in order to avoid unstable activation patterns and “spiky” reconstructed signals often produced by the currently used sparse solvers. The joint spatio-temporal model leads to a cost function with an ?1?2-norm regularizer whose minimization can be reduced to a convex second-order cone programming (SOCP) problem and efficiently solved using the interior-point method. The efficient computation of the SOCP problem allows us to implement permutation tests for estimating statistical significance of the inverse solution. Validation with simulated and real MEG data shows that the proposed solver yields source time course estimates qualitatively similar to those obtained through dipole fitting, but without the need to specify the number of dipole sources in advance. Furthermore, the ?1?2-norm solver achieves fewer false positives and a better representation of the source locations than the conventional ?2 minimum-norm estimates. PMID:18603008

We propose a novel ?1?2-norm inverse solver for estimating the sources of EEG/MEG signals. Based on the standard ?1-norm inverse solvers, this sparse distributed inverse solver integrates the ?1-norm spatial model with a temporal model of the source signals in order to avoid unstable activation patterns and “spiky” reconstructed signals often produced by the currently used sparse solvers. The joint spatio-temporal model leads to a cost function with an ?1?2-norm regularizer whose minimization can be reduced to a convex second-order cone programming (SOCP) problem and efficiently solved using the interior-point method. The efficient computation of the SOCP problem allows us to implement permutation tests for estimating statistical significance of the inverse solution. Validation with simulated and human MEG data shows that the proposed solver yields source time course estimates qualitatively similar to those obtained through dipole fitting, but without the need to specify the number of dipole sources in advance. Furthermore, the ?1?2-norm solver achieves fewer false positives and a better representation of the source locations than the conventional ?2 minimum-norm estimates. PMID:18979728

Spatio-temporal change modeling of our ecosystems is critical for environmental conservation. Open access to remote sensing satellite image archives provides new opportunities for change modeling, such as near real-time change monitoring with long term image time series. Newly developed time series analysis methods allow the detection of quantitative changes in trend and seasonality for each pixel of the image. A drawback of pure time series analysis is that spatial dependence is neglected. There are several spatio-temporal statistical approaches to incorporate spatial context. One method is to build hierarchical models with spatial effects for time series parameters. Other methods include representing regression parameters as spatially correlated random fields, or integrating spatial autoregressive models to time series analysis. Apart from spatio-temporal statistical modeling, the results can be further improved by qualification of detected change points with their spatio-temporal neighbors. Spatio-temporal modeling approaches are typically complex and large in scale, and call for new data management and analysis tools. Remote sensing satellite images, which are continuous and regular in space and time, can naturally be represented as three- or four-dimensional arrays for spatio-temporal data management and analysis. The developed spatio-temporal statistical algorithms can be flexibly applied within array partitions that span the relevant array-based dimensions. This study investigates the potential of array-based Data Data Management and Analytic Software (DMAS) for fast data access, data integration and large-scale complex spatio-temporal analysis. A study case is developed in near-real time deforestation monitoring in Amazonian rainforest with long-term 250 m, 8-day resolution MODIS image time series. A novel spatio-temporal change modeling process is being developed and implemented in DMAS to realize rapid and automated analysis of satellite image time series for forest disturbance detection. The study expects results that improve over a pure time series analysis approach, and that is practically applicable to massive complex spatio-temporal data.

Background Recently, the availability of high-resolution microscopy together with the advancements in the development of biomarkers as reporters of biomolecular interactions increased the importance of imaging methods in molecular cell biology. These techniques enable the investigation of cellular characteristics like volume, size and geometry as well as volume and geometry of intracellular compartments, and the amount of existing proteins in a spatially resolved manner. Such detailed investigations opened up many new areas of research in the study of spatial, complex and dynamic cellular systems. One of the crucial challenges for the study of such systems is the design of a well stuctured and optimized workflow to provide a systematic and efficient hypothesis verification. Computer Science can efficiently address this task by providing software that facilitates handling, analysis, and evaluation of biological data to the benefit of experimenters and modelers. Results The Spatio-Temporal Simulation Environment (STSE) is a set of open-source tools provided to conduct spatio-temporal simulations in discrete structures based on microscopy images. The framework contains modules to digitize, represent, analyze, and mathematically model spatial distributions of biochemical species. Graphical user interface (GUI) tools provided with the software enable meshing of the simulation space based on the Voronoi concept. In addition, it supports to automatically acquire spatial information to the mesh from the images based on pixel luminosity (e.g. corresponding to molecular levels from microscopy images). STSE is freely available either as a stand-alone version or included in the linux live distribution Systems Biology Operational Software (SB.OS) and can be downloaded from http://www.stse-software.org/. The Python source code as well as a comprehensive user manual and video tutorials are also offered to the research community. We discuss main concepts of the STSE design and workflow. We demonstrate it's usefulness using the example of a signaling cascade leading to formation of a morphological gradient of Fus3 within the cytoplasm of the mating yeast cell Saccharomyces cerevisiae. Conclusions STSE is an efficient and powerful novel platform, designed for computational handling and evaluation of microscopic images. It allows for an uninterrupted workflow including digitization, representation, analysis, and mathematical modeling. By providing the means to relate the simulation to the image data it allows for systematic, image driven model validation or rejection. STSE can be scripted and extended using the Python language. STSE should be considered rather as an API together with workflow guidelines and a collection of GUI tools than a stand alone application. The priority of the project is to provide an easy and intuitive way of extending and customizing software using the Python language. PMID:21527030

SpatioTemporal Continuity and Physical Object Identity Thomas W. Smythe I One important answer to the question of how we are to identify material objects as identical through time is that material objects must preserve spatio-temporal (s... at the same spot or position. It is the purpose of this paper to investigate this common conception. In order to see how closely s-t continuity is re­ lated to the numerical identity of material objects through time I shall try to defend the contrary view...

It is known that focusing of an acoustic field by a time-reversal mirror (TRM) is equivalent to a spatio-temporal matched filter under conditions where the Green's function of the field satisfies reciprocity and is time invariant, i.e. the Green's function is independent of the choice of time origin. In this letter, it is shown that both reciprocity and time invariance can be replaced by a more general constraint on the Green's function that allows a TRM to implement the spatio-temporal matched filter even when conditions are time varying.

It is known that focusing of an acoustic field by a time-reversal mirror (TRM) is equivalent to a spatio-temporal matched filter under conditions where the Green's function of the field satisfies reciprocity and is time invariant, i.e., the Green's function is independent of the choice of time origin. In this letter, it is shown that both reciprocity and time invariance can be replaced by a more general constraint on the Green's function that allows a TRM to implement the spatio-temporal matched filter even when conditions are time varying.

We propose a set of tools for spatio-temporal real-time analysis of dynamic scenes. It is designed to improve the grounding\\u000a situation of autonomous agents in (simulated) physical domains. We introduce a knowledge processing pipeline ranging from\\u000a relevance-driven compilation of a qualitative scene description to a knowledge-based detection of complex event and action\\u000a sequences, conceived as a spatio-temporal pattern-matching problem. A

The time series remote sensing data and meteorological satellite data offer new opportunities for understanding the earth system. Spatio-temporal data clustering becomes a kind of idea tool to explore huge data space of spatio-temporal data. Because there are many uncertainties in the huge spatio-temporal data, including fuzziness and randomness, the spatio-temporal clustering methods with uncertainties are needed. Based on type-2

Non-contact based near-infrared (NIR) optical imaging devices are developed for non-invasive imaging of deep tissues in various clinical applications. Most of these devices focus on obtaining the spatial information for anatomical co-registration of blood vessels as in sub-surface vein localization applications. In the current study, the anatomical co-registration of blood vessels based on spatio-temporal features was performed using NIR optical imaging without the use of external contrast agents. A 710 nm LED source and a compact CCD camera system were employed during simple cuff (0 to 60 mmHg) experiment in order to acquire the dynamic NIR data from the dorsum of a hand. The spatio-temporal features of dynamic NIR data were extracted from the cuff experimental study to localize vessel according to blood dynamics. The blood vessels shape is currently reconstructed from the dynamic data based on spatio-temporal features. Demonstrating the spatio-temporal feature of blood dynamic imaging using a portable non-contact NIR imaging device without external contrast agents is significant for applications such as peripheral vascular diseases.

1 A hypercube-based data structure for spatio-temporal exploration and analysis Pierre Marchand database (MDDB) approach to support spatio-temporal data exploration and analysis (STEA). MDDB STEA can be achieved. We thus propose a new data structure which implements a spatio- temporal

TELEGEOMATIC SYSTEM AND REAL TIME SPATIO-TEMPORAL DATABASE Sylvie SERVIGNE *, Tullio TANZI processing systems able to exploit spatio- temporal and real time data. These data are issued from sensors spatio-temporal and real time data. These data are issued from sensors and fixed or mobile systems

The global geospatial community is investing substantial effort in providing tools for geospatial data-quality information analysis and systematizing the criteria for geospatial data quality. The importance of these activities is increasing, especially in the last decade, which has witnessed an enormous expansion of geospatial data use in general and especially among mass users. Although geospatial data producers are striving to define and present data-quality standards to users and users increasingly need to assess the fitness for use of the data, the success of these activities is still far from what is expected or required. As a consequence, neglect or misunderstanding of data quality among users results in misuse or risks. This paper presents an aid in spatio-temporal quality evaluation through the use of spatio-temporal evaluation matrices (STEM) and the index of spatio-temporal anticipations (INSTANT) matrices. With the help of these two simple tools, geospatial data producers can systematically categorize and visualize the granularity of their spatio-temporal data, and users can present their requirements in the same way using business intelligence principles and a Web 2.0 approach. The basic principles and some examples are presented in the paper, and potential further applied research activities are briefly described.

Spatio-Temporal Signal Recovery from Political Tweets in Indonesia Anisha Mazumder, Arun Das activity in the provinces of Indonesia. Based on analysis of radical/counter radical sentiments expressed in tweets by Twitter users, we create a Heat Map of Indonesia which visually demonstrates the degree

Salient object perception is the process of sensing the salient information from the spatio-temporal visual scenes, which is a rapid pre-attention mechanism for the target location in a visual smart sensor. In recent decades, many successful models of visual saliency perception have been proposed to simulate the pre-attention behavior. Since most of the methods usually need some ad hoc parameters or high-cost preprocessing, they are difficult to rapidly detect salient object or be implemented by computing parallelism in a smart sensor. In this paper, we propose a novel spatio-temporal saliency perception method based on spatio-temporal hypercomplex spectral contrast (HSC). Firstly, the proposed HSC algorithm represent the features in the HSV (hue, saturation and value) color space and features of motion by a hypercomplex number. Secondly, the spatio-temporal salient objects are efficiently detected by hypercomplex Fourier spectral contrast in parallel. Finally, our saliency perception model also incorporates with the non-uniform sampling, which is a common phenomenon of human vision that directs visual attention to the logarithmic center of the image/video in natural scenes. The experimental results on the public saliency perception datasets demonstrate the effectiveness of the proposed approach compared to eleven state-of-the-art approaches. In addition, we extend the proposed model to moving object extraction in dynamic scenes, and the proposed algorithm is superior to the traditional algorithms. PMID:23482090

Use of the UCLA CONQUEST (CONtent-based Querying in Space and Time) is reviewed for performance of automatic cyclone extraction and detection of spatio-temporal blocking conditions on MPP. CONQUEST is a data analysis environment for knowledge and data mining to aid in high-resolution modeling of climate modeling.

This paper addresses the problem of detecting and tracking moving objects in digital image sequences. The main goal is to detect and select mobile objects in a scene, construct the trajectories, and eventually reconstruct the target objects or their signatures. It is assumed that the image sequences are acquired from imaging sensors. The method is based on spatio-temporal continuous wavelet

We propose a new fast algorithm for block motion vector (MV) estimation based on the correlations of the MVs existing in spatially and temporally adjacent as well as hierarchically related blocks. We first establish a basic framework by introducing new algorithms based on spatial correlation and then spatio-temporal correlations before integrating them with a multiresolution scheme for the ultimate algorithm.

Spatial or temporal data mining tasks are performed in the context of the relevant space, defined by a spatial neighborhood, and the relevant time period, defined by a specific time interval. Furthermore, when mining large spatio-temporal datasets, interesting patterns typically emerge where the dataset is most dynamic. This dissertation is…

We propose an extension of map algebra to three dimensions for spatio-temporal data handling. This approach yields a new class of map algebra functions that we call "cube functions." Whereas conventional map algebra functions operate on data layers representing two-dimensional space, cube functions operate on data cubes representing two-dimensional space over a third-dimensional period of time. We describe the prototype implementation of a spatio-temporal data structure and selected cube function versions of conventional local, focal, and zonal map algebra functions. The utility of cube functions is demonstrated through a case study analyzing the spatio-temporal variability of remotely sensed, southeastern U.S. vegetation character over various land covers and during different El Nin??o/Southern Oscillation (ENSO) phases. Like conventional map algebra, the application of cube functions may demand significant data preprocessing when integrating diverse data sets, and are subject to limitations related to data storage and algorithm performance. Solutions to these issues include extending data compression and computing strategies for calculations on very large data volumes to spatio-temporal data handling.

The perceptual coherence of auditory and visual information is achieved by integrative brain processes. Specialized single neurons with spatial and temporal interactions of auditory and visual stimuli have been demonstrated by several neurophysiological studies. The present, psychophysical, study investigates possible perceptual correlates of these neuronal features. Subjects had to indicate the point of subjective spatial alignment (PSSA) for a horizontally moving visual stimulus that crossed the position of a stationary sound source. Auditory and visual stimuli consisted of periodic pulses that were systematically varied in their phase relationship or repetition rate. PSSAs obtained for continuous visual stimuli served as a reference. When sound and light pulses were coincident in phase at a repetition rate of 2 Hz, PSSAs were shifted by approximately 3 degrees in a direction opposite to the movement of the visual stimulus (with respect to the reference condition). This shift markedly decreased when the temporal disparity exceeded approximately 100 ms and disappeared near phase opposition (250 ms disparity). With 4 Hz repetition rate (temporal disparity < or =125 ms), there was no significant effect of phase relationship on PSSAs, but still an approximately constant shift with respect to the reference value. Variation of the repetition rate resulted in almost constant shifts in PSSA of approximately 3 degrees between 1 and 4 Hz and a linear decrease (slope 0.27 degrees /Hz) with higher repetition rates. These results suggest a spatio-temporal 'window' for auditory-visual integration, that extends over approximately 100 ms and approximately 3 degrees : when auditory and visual stimuli are within this window, they are always perceived as spatially coincident. These psychophysical findings may be related to properties of bimodal neurons such as have been demonstrated by neurophysiological recordings in midbrain and cortex. PMID:11275285

This paper presents preliminary work in using data mining techniques to find interesting spatio-temporal patterns from Earth Science data. The data consists of time series measurements for various Earth science and climate variables (e.g. soil moisture, temperature, and precipitation), along with additional data from existing ecosystem models (e.g. Net Primary Production). The ecological patterns of interest include associations, clusters, predictive

It is known that focusing of an acoustic field by a time-reversal mirror (TRM) is equivalent to a spatio-temporal matched filter under conditions where the Green's function of the field satisfies reciprocity and is time invariant, i.e. the Green's function is independent of the choice of time origin. In this letter, it is shown that both reciprocity and time invariance

\\u000a We propose a new approach to maternal ECG suppression and fetal QRS detection in the multi-channel maternal abdominal bioelectric\\u000a signals. First, a single-channel method based on template subtraction is applied to suppress the maternal ECG in the respective\\u000a channels. Then we use spatial or spatio-temporal filtering to enhance the fetal QRS complexes. Finally, the QRS detection\\u000a is performed. In the

Spatio-temporal measurements of wind-driven short-gravity capillary waves are reported for a wide range of experimental conditions, including wind, rain and surface slicks. The experiments were conducted in the Hamburg linear wind\\/wave flume in cooperation with the Institute of Oceanography at the University of Hamburg, Germany. Both components of the slope field were measured optically at a fetch of 14.4 m

Often, in environmental data collection, data arise from two sources: numerical models and monitoring networks. The first source provides predictions at the level of grid cells, while the second source gives measurements at points. The first is characterized by full spatial coverage of the region of interest, high temporal resolution, no missing data but consequential calibration concerns. The second tends to be sparsely collected in space with coarser temporal resolution, often with missing data but, where recorded, provides, essentially, the true value. Accommodating the spatial misalignment between the two types of data is of fundamental importance for both improved predictions of exposure as well as for evaluation and calibration of the numerical model. In this article we propose a simple, fully model-based strategy to downscale the output from numerical models to point level. The static spatial model, specified within a Bayesian framework, regresses the observed data on the numerical model output using spatially-varying coefficients which are specified through a correlated spatial Gaussian process.As an example, we apply our method to ozone concentration data for the eastern U.S. and compare it to Bayesian melding (Fuentes and Raftery 2005) and ordinary kriging (Cressie 1993; Chilès and Delfiner 1999). Our results show that our method outperforms Bayesian melding in terms of computing speed and it is superior to both Bayesian melding and ordinary kriging in terms of predictive performance; predictions obtained with our method are better calibrated and predictive intervals have empirical coverage closer to the nominal values. Moreover, our model can be easily extended to accommodate for the temporal dimension. In this regard, we consider several spatio-temporal versions of the static model. We compare them using out-of-sample predictions of ozone concentration for the eastern U.S. for the period May 1-October 15, 2001. For the best choice, we present a summary of the analysis. Supplemental material, including color versions of Figures 4, 5, 6, 7, and 8, and MCMC diagnostic plots, are available online. PMID:21113385

The transition to three-dimensional and unsteady flow in an annulus with a discrete heat source on the inner cylinder is studied numerically. For large applied heat flux through the heater (large Grashof number Gr), there is a strong wall plume originating at the heater that reaches the top and forms a large scale axisymmetric wavy structure along the top. For Gr ? 6 × 109, this wavy structure becomes unstable to three-dimensional instabilities with high azimuthal wavenumbers m ˜ 30, influenced by mode competition within an Eckhaus band of wavenumbers. Coexisting with some of these steady three-dimensional states, solution branches with localized defects break parity and result in spatio-temporal dynamics. We have identified two such time dependent states. One is a limit cycle that while breaking spatial parity, retains spatio-temporal parity. The other branch corresponds to quasi-periodic states that have globally broken parity.

A common method to explore the somatosensory function of the brain is to relate skin stimuli to neurophysiological recordings. However, interaction with the skin involves complex mechanical effects. Variability in mechanically induced spike responses is likely to be due in part to mechanical variability of the transformation of stimuli into spiking patterns in the primary sensors located in the skin. This source of variability greatly hampers detailed investigations of the response of the brain to different types of mechanical stimuli. A novel stimulation technique designed to minimize the uncertainty in the strain distributions induced in the skin was applied to evoke responses in single neurons in the cat. We show that exposure to specific spatio-temporal stimuli induced highly reproducible spike responses in the cells of the cuneate nucleus, which represents the first stage of integration of peripheral inputs to the brain. Using precisely controlled spatio-temporal stimuli, we also show that cuneate neurons, as a whole, were selectively sensitive to the spatial and to the temporal aspects of the stimuli. We conclude that the present skin stimulation technique based on localized differential tractions greatly reduces response variability that is exogenous to the information processing of the brain and hence paves the way for substantially more detailed investigations of the brain's somatosensory system. PMID:24451390

High bacterial density and diversity near plant roots has been attributed to rhizodeposit compounds that serve as both energy sources and signal molecules. However, it is unclear if and how specific rhizodeposit compounds influence bacterial diversity. We silenced the biosynthesis of isoflavonoids, a major component of soybean rhizodeposits, using RNA interference in hairy-root composite plants, and examined changes in rhizosphere bacteriome diversity. We used successive sonication to isolate soil fractions from different rhizosphere zones at two different time points and analyzed denaturing gradient gel electrophoresis profiles of 16S ribosomal RNA gene amplicons. Extensive diversity analysis of the resulting spatiotemporal profiles of soybean bacterial communities indicated that, indeed, isoflavonoids significantly influenced soybean rhizosphere bacterial diversity. Our results also suggested a temporal gradient effect of rhizodeposit isoflavonoids on the rhizosphere. However, the hairy-root transformation process itself significantly altered rhizosphere bacterial diversity, necessitating appropriate additional controls. Gene silencing in hairy-root composite plants combined with successive sonication is a useful tool to determine the spatiotemporal effect of specific rhizodeposit compounds on rhizosphere microbial communities. PMID:25303334

A spatio-temporal equalizer has been conceived as an improved means of suppressing multipath effects in the reception of aeronautical telemetry signals, and may be adaptable to radar and aeronautical communication applications as well. This equalizer would be an integral part of a system that would also include a seven-element planar array of receiving feed horns centered at the focal point of a paraboloidal antenna that would be nominally aimed at or near the aircraft that would be the source of the signal that one seeks to receive (see Figure 1). This spatio-temporal equalizer would consist mostly of a bank of seven adaptive finite-impulse-response (FIR) filters one for each element in the array - and the outputs of the filters would be summed (see Figure 2). The combination of the spatial diversity of the feedhorn array and the temporal diversity of the filter bank would afford better multipath-suppression performance than is achievable by means of temporal equalization alone. The seven-element feed array would supplant the single feed horn used in a conventional paraboloidal ground telemetry-receiving antenna. The radio-frequency telemetry signals re ceiv ed by the seven elements of the array would be digitized, converted to complex baseband form, and sent to the FIR filter bank, which would adapt itself in real time to enable reception of telemetry at a low bit error rate, even in the presence of multipath of the type found at many flight test ranges.

Nitrate (NO3-) is considered the most prevalent contaminant in groundwater (GW). NO3- in GW shows significant spatio-temporal variability which comes from interaction among multiple geophysical factors such as source availability (land use), thickness and composition of the vadose zone, types of aquifers (confined or unconfined), aquifer heterogeneity (geological and alluvial), and precipitation characteristics etc. The present work seeks to describe the spatio-temporal variability of NO3- at multiple scales in two different hydrogeologic settings— the Trinity and Ogallala Aquifers in Texas at three spatial scales, fine (25 km.×25 km.), intermediate (50 km.×50 km.), and coarse (100 km.×100 km.) grids. An entropy-based approach was used to analyze spatial-temporal variability of NO3- within the aquifers. The Hurst exponent was used to evaluate the long-term persistence and trend in the variability of NO3-. The results demonstrate that the spatial variability of NO3- is controlled by the effect of soil type, irrigation-pumping, and local flow at the small scale and by the complex interactions between rivers and aquifers along with land use at the intermediate scale, and by lithology and geology at the coarse scale. The trends of variability of NO3- show long term persistence at the intermediate and coarse scales.

Novel arboviruses, including new serotypes of bluetongue virus, are isolated intermittently from cattle and insects in northern Australia. These viruses are thought to be introduced via windborne dispersal of Culicoides from neighbouring land masses to the north. We used the HYSPLIT particle dispersal model to simulate the spatio-temporal patterns of Culicoides dispersal into northern Australia from nine putative source sites across Indonesia, Timor-Leste and Papua New Guinea. Simulated dispersal was found to be possible from each site, with the islands of Timor and Sumba highlighted as the likely principal sources and February the predominant month of dispersal. The results of this study define the likely spatial extent of the source and arrival regions, the relative frequency of dispersal from the putative sources and the temporal nature of seasonal winds from source sites into arrival regions. Importantly, the methodology and results may be applicable to other insect and pathogen incursions into northern Australia. PMID:23642857

Several aspects of spatiotemporal databases have been explored in past decades, ranging from basic data structure to query processing and indexing. But today, operational temporal GIS does not exist. The key impediments have been the complexity of integrating space and time and the lack of standards. OpenGIS standards for simple feature access (spatial type) do exist, but unlike the spatial type, standards for spatiotemporal data type do not exist. This paper explores a new approach to modeling space and time to provide the basis for implementing a temporal GIS. This approach is based on the concept of data types in databases. A data type provides constructors, accessors, and operators. Most commercial and open source databases provide data types to deal with the spatial component of a GIS, called spatial type. Oracle Spatial, DB2 Spatial Extender and Informix Spatial DataBlade, ST_Geometry for PostgreSQL and Oracle from Esri, PostGIS for PostgreSQL, etc., are some examples. This new spatiotemporal data type is called spatiotemporal type (STT). This STT is implemented in PostgreSQL, an open source relational database management system. The STT is an extension of Esri's ST_Geometry type for PostgreSQL, in which each spatial object has a life span. Constructors, accessors, and relational functions are provided to create STT and support spatial, spatiotemporal, and temporal queries. Some functions are extended based on OpenGIS standards for the spatial type. Examples are provided to demonstrate the application of these functions. The paper concludes with limitations and challenges of implementing STT.

We study evolutionary processes induced by spatio-temporal dynamics in prebiotic evolution. Using numerical simulations, we demonstrate that hypercycles emerge from complex interaction structures in multispecies systems. In this work, we also find that ‘hypercycle hybrid’ protects the hypercycle from its environment during the growth process. There is little selective advantage for one hypercycle to maintain coexistence with others. This brings the possibility of the outcompetition between hypercycles resulting in the negative effect on information diversity. To enrich the information in hypercycles, symbiosis with parasites is suggested. It is shown that symbiosis with parasites can play an important role in the prebiotic immunology.

A simple model for temporal bursting is introduced. This model invokes either dynamic or random forcing of a bifurcation parameter of some simple dynamical system in a way that makes the bifurcation parameter spend suitable amounts of time below and above the bifurcation threshold. This model is extended to coupled map lattices to produce spontaneous spatio-temporal burstings. It models physical systems which are embedded in a random background that is statistically homogeneous in space and time. An application of this model to optical turbulence is discussed. {copyright} {ital 1996 American Institute of Physics.}

In the present study, the concentrations of VOC were measured using Proton Transfer Reaction Mass Spectrometer, together with NOx, NO2, NO, SO2, CO, and PM10 during winter 2014 in Belgrade, Serbia. For the purpose of source apportionment, receptor models Positive Matrix Factorization and Unmix were applied to the obtained dataset, both resolving six profiles. The reliable identification of pollutant sources, description of their characteristics, and estimation of their spatio-temporal distribution are presented through comprehensive analysis and comparison of receptor model solutions, with respect to meteorological data, planetary boundary layer height, and regional and long-range transport. For emissions from petrochemical industry and oil refinery a significant contribution of transport is observed, and hybrid receptor models were applied for identification of potential non-local source regions.

Scale-free model for spatio-temporal distribution of outbreaks of avian influenza Michael Small influenza outbreaks among wild and domestic birds, we show that this model is not appropriate. We find the global spatio-temporal distribution of avian influenza cases in both wild and domestic birds and find

averagely divide the spatio- temporal volume into atomic blocks. Considering the fact that mutual analysis of densely crowded envi- ronments such as subways, universities, railway stations and stadiums has the spatio-temporal volume into atomic blocks. Considering the fact that mutual interference of several human

This special issue on geographic boundary analysis in spatio-temporal epidemiology and public health seeks, conduct and assess the effectiveness of public health interventions. Consider some examplesThe emerging role and benefits of boundary analysis in spatio- temporal epidemiology and public

A Spatio-Temporal Memory Based on SOMs with Activity Diffusion Neil R. Eulianoa and Jose CEngineering Laboratory, University of Florida Gainesville, FL 32611 This paper discusses the use of the biologically inspired concept of activity diffusion to create a spatio-temporal memory in the SOM and neural gas

That physiological oscillations of various frequencies are present in fMRI signals is the rule, not the exception. Herein, we propose a novel theoretical framework, spatio-temporal Granger causality, which allows us to more reliably and precisely estimate the Granger causality from experimental datasets possessing time-varying properties caused by physiological oscillations. Within this framework, Granger causality is redefined as a global index measuring the directed information flow between two time series with time-varying properties. Both theoretical analyses and numerical examples demonstrate that Granger causality is a monotonically increasing function of the temporal resolution used in the estimation. This is consistent with the general principle of coarse graining, which causes information loss by smoothing out very fine-scale details in time and space. Our results confirm that the Granger causality at the finer spatio-temporal scales considerably outperforms the traditional approach in terms of an improved consistency between two resting-state scans of the same subject. To optimally estimate the Granger causality, the proposed theoretical framework is implemented through a combination of several approaches, such as dividing the optimal time window and estimating the parameters at the fine temporal and spatial scales. Taken together, our approach provides a novel and robust framework for estimating the Granger causality from fMRI, EEG, and other related data. PMID:23643924

PCRaster Python is a software framework for building spatio-temporal models of land surface processes (Karssenberg, Schmitz, Salamon, De Jong, & Bierkens, 2010; PCRaster, 2012). Building blocks of models are spatial operations on raster maps, including a large suite of operations for water and sediment routing. These operations, developed in C++, are available to model builders as Python functions. Users create models by combining these functions in a Python script. As construction of large iterative models is often difficult and time consuming for non-specialists in programming, the software comes with a set of Python framework classes that provide control flow for static modelling, temporal modelling, stochastic modelling using Monte Carlo simulation, and data assimilation techniques including the Ensemble Kalman filter and the Particle Filter. A framework for integrating model components with different time steps and spatial discretization is currently available as a prototype (Schmitz, de Jong, & Karssenberg, in review). The software includes routines for visualisation of stochastic spatio-temporal data for prompt, interactive, visualisation of model inputs and outputs. Visualisation techniques include animated maps, time series, probability distributions, and animated maps with exceedance probabilities. The PCRaster Python software is used by researchers from a large range of disciplines, including hydrology, ecology, sedimentology, and land use change studies. Applications include global scale hydrological modelling and error propagation in large-scale land use change models. The software runs on MS Windows and Linux operating systems, and OS X (under development).

An array of SQUID biomagentometers may be used to measure the spatio-temporal neuromagnetic field produced by the brain in response to a given sensory stimulus. A popular model for the neural activity that produces these fields is a set of current dipoles. We present here a common linear algebraic framework for three common spatio-temporal dipole models: moving and rotating dipoles, rotating dipoles with fixed location, and dipoles with fixed orientation and location. Our intent here is not to argue the merits of one model over another, but rather show how each model may be solved efficiently, and within the same framework as the others. In all cases, we assume that the location, orientation, and magnitude of the dipoles are unknown. We present the parameter estimation problem for these three models in a common framework, and show how, in each case, the problem may be decomposed into the estimation of the dipole locations using nonlinear minimization followed by linear estimation of the associated moment time series. Numerically efficient means of calculating the cost function are presented, and problems of model order selection and missing moments are also investigated. The methods described are demonstrated in a simulated application to a three dipole problem. 21 refs., 2 figs., 1 tab.

Networks of well-known dynamical units but unknown interaction topology arise across various fields of biology, including genetics, ecology, and neuroscience. The collective dynamics of such networks is often sensitive to the presence (or absence) of individual interactions, but there is usually no direct way to probe for their existence. Here we present an explicit method for reconstructing interaction networks of leaky integrate-and-fire neurons from the spike patterns they exhibit in response to external driving. Given the dynamical parameters are known, the approach works well for networks in simple collective states but is also applicable to networks exhibiting complex spatio-temporal spike patterns. In particular, stationarity of spiking time series is not required. PMID:21344004

Analysing human gait has found considerable interest in recent computer vision research. So far, however, contributions to this topic exclusively dealt with the tasks of person identification or activity recognition. In this paper, we consider a different application for gait analysis and examine its use as a means of deducing the physical well-being of people. The proposed method is based on transforming the joint motion trajectories using wavelets to extract spatio-temporal features which are then fed as input to a vector quantiser; a self-organising map for classification of walking patterns of individuals with and without pathology. We show that our proposed algorithm is successful in extracting features that successfully discriminate between individuals with and without locomotion impairment.

Trichel pulses are investigated using a needle-to-plane electrode geometry at low pressure. The evolution of current and voltage, the spatio-temporal discharge images of Trichel pulse are measured. The rising time and duration time in a pulse are about 10 ?s and several tens of microseconds, respectively. One period of pulse can be divided into three stages: the stage preceding cathode breakdown, cathode glow formation, and discharge decaying process. Besides a cathode glow and a dark space, an anode glow is also observed. The emission spectra mainly originate from the C3?u ? B3?g transition for nitrogen. In addition, the capacitances in parallel connected with the discharge cell have important influence on the pulsing frequency.

We analyzed the spatio-temporal structure of hooded gull flocks with a portable stereo camera system. The 3-dimensional positions of individuals were reconstructed from pairs of videos. The motions of each individual were analyzed, and both gliding and flapping motions were quantified based on the velocity time series. We analyzed the distributions of the nearest neighbor’s position in terms of coordinates based on each individual’s motion. The obtained results were consistent with the aerodynamic interaction between individuals. We characterized the leader-follower relationship between individuals by a delay time to mimic the direction of a motion. A relation between the delay time and a relative position was analyzed quantitatively, which suggested the basic properties of the formation flight that maintains order in the flock. PMID:24339960

Accurate estimation of population at risk from hazards and effective emergency management of events require not just appropriate spatio-temporal modelling of hazards but also of population. While much recent effort has been focused on improving the modelling and predictions of hazards (both natural and anthropogenic), there has been little parallel advance in the measurement or modelling of population statistics. Different hazard types occur over diverse temporal cycles, are of varying duration and differ significantly in their spatial extent. Even events of the same hazard type, such as flood events, vary markedly in their spatial and temporal characteristics. Conceptually and pragmatically then, population estimates should also be available for similarly varying spatio-temporal scales. Routine population statistics derived from traditional censuses or surveys are usually static representations in both space and time, recording people at their place of usual residence on census/survey night and presenting data for administratively defined areas. Such representations effectively fix the scale of population estimates in both space and time, which is unhelpful for meaningful risk management. Over recent years, the Pop24/7 programme of research, based at the University of Southampton (UK), has developed a framework for spatio-temporal modelling of population, based on gridded population surfaces. Based on a data model which is fully flexible in terms of space and time, the framework allows population estimates to be produced for any time slice relevant to the data contained in the model. It is based around a set of origin and destination centroids, which have capacities, spatial extents and catchment areas, all of which can vary temporally, such as by time of day, day of week, season. A background layer, containing information on features such as transport networks and landuse, provides information on the likelihood of people being in certain places at specific times. Unusual patterns associated with special events can also be modelled and the framework is fully volume preserving. Outputs from the model are gridded population surfaces for the specified time slice, either for total population or by sub-groups (e.g. age). Software to implement the models (SurfaceBuilder247) has been developed and pre-processed layers for typical time slices for England and Wales in 2001 and 2006 are available for UK academic purposes. The outputs and modelling framework from the Pop24/7 programme provide significant opportunities for risk management applications. For estimates of mid- to long-term cumulative population exposure to hazards, such as in flood risk mapping, populations can be produced for numerous time slices and integrated with flood models. For applications in emergency response/ management, time-specific population models can be used as seeds for agent-based models or other response/behaviour models. Estimates for sub-groups of the population also permit exploration of vulnerability through space and time. This paper outlines the requirements for effective spatio-temporal population models for risk management. It then describes the Pop24/7 framework and illustrates its potential for risk management through presentation of examples from natural and anthropogenic hazard applications. The paper concludes by highlighting key challenges for future research in this area.

Trichel pulses are investigated using a needle-to-plane electrode geometry at low pressure. The evolution of current and voltage, the spatio-temporal discharge images of Trichel pulse are measured. The rising time and duration time in a pulse are about 10??s and several tens of microseconds, respectively. One period of pulse can be divided into three stages: the stage preceding cathode breakdown, cathode glow formation, and discharge decaying process. Besides a cathode glow and a dark space, an anode glow is also observed. The emission spectra mainly originate from the C{sup 3}?{sub u} ? B{sup 3}?{sub g} transition for nitrogen. In addition, the capacitances in parallel connected with the discharge cell have important influence on the pulsing frequency.

OLAP (On Line Analytical Processing) is a set of techniques and operators to facilitate the data analysis usually stored in a data warehouse. In this paper, we extend the functionality of an OLAP operator known as Map Cube with the definition and incorporation of a function that allows the formulation of spatio-temporal queries. For example, consider a data warehouse about crimes that includes data about the places where the crimes were committed. Suppose we want to find and visualize the trajectory (a trajectory is just the path that an object follows through space as a function of time) of the crimes of a suspect beginning with his oldest crime and ending with his most recent one. In order to meet this requirement, we extend the Map Cube operator.

The aortic sinus vortex is a classical flow structure of significant importance to aortic valve dynamics and the initiation and progression of calific aortic valve disease. We characterize the spatio-temporal characteristics of aortic sinus voxtex dynamics in relation to the viscosity of blood analog solution as well as heart rate. High resolution time-resolved (2KHz) particle image velocimetry was conducted to capture 2D particle streak videos and 2D instantaneous velocity and streamlines along the sinus midplane using a physiological but rigid aorta model fitted with a porcine bioprosthetic heart valve. Blood analog fluids used include a water-glycerin mixture and saline to elucidate the sensitivity of vortex dynamics to viscosity. Experiments were conducted to record 10 heart beats for each combination of blood analog and heart rate condition. Results show that the topological characteristics of the velocity field vary in time-scales as revealed using time bin averaged vectors and corresponding instantaneous streamlines. There exist small time-scale vortices and a large time-scale main vortex. A key flow structure observed is the counter vortex at the upstream end of the sinus adjacent to the base (lower half) of the leaflet. The spatio-temporal complexity of vortex dynamics is shown to be profoundly influenced by strong leaflet flutter during systole with a peak frequency of 200Hz and peak amplitude of 4 mm observed in the saline case. While fluid viscosity influences the length and time-scales as well as the introduction of leaflet flutter, heart rate influences the formation of counter vortex at the upstream end of the sinus. Higher heart rates are shown to reduce the strength of the counter vortex that can greatly influence the directionality and strength of shear stresses along the base of the leaflet. This study demonstrates the impact of heart rate and blood analog viscosity on aortic sinus hemodynamics. PMID:25067881

The space-time interpolation of precipitation has significant contribution to river control,reservoir operations, forestry interest and flash flood watches etc. The changes in environmental covariates and spatial covariates make space-time estimation of precipitation a challenging task. In our earlier paper [1], we used transformed hirarchical Bayesian sapce-time interpolation method for predicting the amount of precipiation. In present paper, we modified the [2] method to include covarites which varaies with respect to space-time. The proposed method is applied to estimating space-time monthly precipitation in the monsoon periods during 1974 - 2000. The 27-years monthly average data of precipitation, temperature, humidity and wind speed are obtained from 51 monitoring stations in Pakistan. The average monthly precipitation is used response variable and temperature, humidity and wind speed are used as time varying covariates. Moreovere the spatial covarites elevation, latitude and longitude of same monitoring stations are also included. The cross-validation method is used to compare the results of transformed hierarchical Bayesian spatio-temporal interpolation with and without including environmental and spatial covariates. The software of [3] is modified to incorprate enviornmental covariates and spatil covarites. It is observed that the transformed hierarchical Bayesian method including covarites provides more accuracy than the transformed hierarchical Bayesian method without including covarites. Moreover, the five potential monitoring cites are selected based on maximum entropy sampaling design approach. References [1] I.Hussain, J.Pilz,G. Spoeck and H.L.Yu. Spatio-Temporal Interpolation of Precipitation during Monsoon Periods in Pakistan. submitted in Advances in water Resources,2009. [2] N.D. Le, W. Sun, and J.V. Zidek, Bayesian multivariate spatial interpolation with data missing by design. Journal of the Royal Statistical Society. Series B (Methodological), 501-510, 1997. [3] N.D. Le, and J.V. Zidek, Statistical analysis of environmental space-time processes, Springer Verlag, (2006), PP. 272-294

Background African animal trypanosomosis (AAT) is a major constraint to sustainable development of cattle farming in sub-Saharan Africa. The habitat of the tsetse fly vector is increasingly fragmented owing to demographic pressure and shifts in climate, which leads to heterogeneous risk of cyclical transmission both in space and time. In Burkina Faso and Ghana, the most important vectors are riverine species, namely Glossina palpalis gambiensis and G. tachinoides, which are more resilient to human-induced changes than the savannah and forest species. Although many authors studied the distribution of AAT risk both in space and time, spatio-temporal models allowing predictions of it are lacking. Methodology/Principal Findings We used datasets generated by various projects, including two baseline surveys conducted in Burkina Faso and Ghana within PATTEC (Pan African Tsetse and Trypanosomosis Eradication Campaign) national initiatives. We computed the entomological inoculation rate (EIR) or tsetse challenge using a range of environmental data. The tsetse apparent density and their infection rate were separately estimated and subsequently combined to derive the EIR using a “one layer-one model” approach. The estimated EIR was then projected into suitable habitat. This risk index was finally validated against data on bovine trypanosomosis. It allowed a good prediction of the parasitological status (r2 = 67%), showed a positive correlation but less predictive power with serological status (r2 = 22%) aggregated at the village level but was not related to the illness status (r2 = 2%). Conclusions/Significance The presented spatio-temporal model provides a fine-scale picture of the dynamics of AAT risk in sub-humid areas of West Africa. The estimated EIR was high in the proximity of rivers during the dry season and more widespread during the rainy season. The present analysis is a first step in a broader framework for an efficient risk management of climate-sensitive vector-borne diseases. PMID:26154506

BLINDSOURCE SEPARATION OF NONLINEAR MIXING MODELS TeÂ­Won Lee Salk Institute, CNL La Jolla, C a new set of learning rules for the nonÂ­ linear blindsource separation problem based on the inÂ­ formation maximization criterion. The mixing model is divided into a linear mixing part and a nonlinear

Global models and observations differ strongly in their spatio-temporal sampling. First, Model results are typical of large gridboxes (100 km), while observations are made over much smaller areas (1 to 10 km). Second, model results are always available in contrast to observations that are intermittent due to sampling strategies, retrieval limitations and instrument failure/maintenance. We investigate the consequences of spatio-temporal sampling for the evaluation of models with observations and find them to be significant (differences up to 100% in monthly or yearly averages due to sampling alone). Using high resolution WRF-Chem and EMEP simulations, we study the impact of evaluating low resolution global models with highly localised observations. Results suggest that significant differences due to the spatial aggregation alone will exist between models and observations, even after averaging data over e.g. a month. When using realistic observational sampling, these differences will be even bigger. Results depend on the concerned observable: a column-integrated property like AOT, easily advected by the flow, will exhibit smaller differences than a surface property like PM2.5, especially if that surface property shows little advection (e.g. number density). We explain these results qualitatively as a consequence of flow structure and aerosol source length-scales. Furthermore, we show that proper temporal collocation of model data with the observations and further spatial aggregation of the observations can reduce (but not entirely remove!) these sampling-induced differences. We point out that even temporal collocation is by no means a standard procedure for researchers and often it is simply assumed that 'over time' issues due to sampling will average out (we show they will not).

Understanding past compositional changes in vegetation provides insight about ecosystem dynamics in response to changing environments. Past vegetation reconstructions rely predominantly on fossil pollen data from sedimentary lake cores, which acts as a proxy record for the surrounding vegetation. Stratigraphic changes in these pollen records allow us to infer changes in composition and species distributions. Pollen records collected from a network of sites allow us to make inference about the spatio-temporal changes in vegetation over thousands of years. However, the complexity of the relationship between pollen deposits and surrounding vegetation, as well as the spatially sparse set of fossil pollen sites are important sources of uncertainty. In addition, uncertainty arises from the carbon dating and age-depth modelling processes. To reconstruct vegetation composition including uncertainty for the Upper Midwestern USA, we build a Bayesian hierarchical model that links vegetation composition to fossil pollen data via a dispersal model. In the calibration phase, we estimate the relationship between vegetation and pollen for the settlement era using Public Land Survey data and a network of pollen records. In the prediction phase, parameter estimates obtained during the calibration phase are used to estimate latent species distributions and relative abundances over the last 2500 years. We account for additional uncertainty in the pollen records by: allowing expert palynologists to identify pre-settlement pollen samples to be included in our calibration data, and through the incorporation of age uncertainty obtained from the Bayesian age-depth model BACON in our prediction data. Resulting spatio-temporal composition and abundance estimates will be used to improve forecasting capabilities of ecosystem models.

We demonstrate a method to automatically extract spatio-temporal descriptions of human faces from synchronized and calibrated\\u000a multi-view sequences. The head is modeled by a time-varying multi-resolution subdivision surface that is fitted to the observed\\u000a person using spatio-temporal multi-view stereo information, as well as contour constraints. The stereo data is utilized by\\u000a computing the normalized correlation between corresponding spatio-temporal image trajectories

In this paper we propose a new method for human action cat- egorization by using an effective combination of a new 3D gradient descriptor with an optic flow descriptor, to represent spatio-temporal interest points. These points are used to rep- resent video sequences using a bag of spatio-temporal visual words, following the successful results achieved in object and scene classification.

In this paper we propose a system for human action tracking and recognition using a robust particle lter-based visual tracker and a novel descriptor, to represent spatio-temporal interest points, based on an eective combination of a new 3D gradient descriptor with an optic ow descriptor. These points are used to represent video sequences using a bag of spatio-temporal visual words,

\\u000a We analyzed the spatio-temporal patterns of the depth EEG recorded from a patient with lateral temporal-lobe epilepsy. Statistical\\u000a analysis based on the Jensen-Shannon entropy (JS-E) as well as linear power spectral analyses were performed. The spatio-temporal\\u000a patterns from JS-E and ? rhythm successfully detected the onset timing of the seizure and revealed the temporal topology of the epileptic focus. The

Conservation of carnivores in an increasingly changing environment is greatly helped by understanding the decision-making processes underlying habitat patch choice. Foraging theory may give us insight into spatio-temporal search patterns and consequent foraging decisions that carnivores make in heterogeneous and fluctuating environments. Constraints placed on central-place foragers in particular are likely to influence both foraging decisions and related spatio-temporal movement

This paper presents a set of methods and techniques for analysis and multidimensional visualisation of crime scenes in a German\\u000a city. As a first step the approach implies spatio-temporal analysis of crime scenes. Against this background a GIS-based application\\u000a is developed that facilitates discovering initial trends in spatio-temporal crime scene distributions even for a GIS untrained\\u000a user. Based on these

This research focuses on the spatio-temporal characteristics of lips and jaw movements and on their relevance for lip-reading, bimodal communication theory and bimodal recognition applications. 3D visible articulatory targets for vowels and consonants are proposed. Relevant modifications on the spatio- temporal consonant targets due to coarticulatory phenomena are exemplified. When visual parameters are added to acoustic ones as inputs to

Understanding food web functioning through the study of natural bio-indicators may constitute a valuable and original approach. In the context of jellyfish proliferation in many overexploited marine ecosystems studying the spatio-temporal foraging patterns of the giant “jellyvore” leatherback turtle turns out to be particularly relevant. Here we analyzed long-term tracking data to assess spatio-temporal foraging patterns in 21 leatherback turtles

The first Modelfest group publication appeared in the SPIE Human Vision and Electronic Imaging conference proceedings in 1999. "One of the group's goals is to develop a public database of test images with threshold data from multiple laboratories for designing and testing HVS (Human Vision Models)." After extended discussions the group selected a set of 45 static images thought to best meet that goal and collected psychophysical detection data which is available on the WEB and presented in the 2000 SPIE conference proceedings. Several groups have used these datasets to test spatial modeling ideas. Further discussions led to the preliminary stimulus specification for extending the database into the temporal domain which was published in the 2002 conference proceeding. After a hiatus of 12 years, some of us have collected spatio-temporal thresholds on an expanded stimulus set of 41 video clips; the original specification included 35 clips. The principal change involved adding one additional spatial pattern beyond the three originally specified. The stimuli consisted of 4 spatial patterns, Gaussian Blob, 4 c/d Gabor patch, 11.3 c/d Gabor patch and a 2D white noise patch. Across conditions the patterns were temporally modulated over a range of approximately 0-25 Hz as well as temporal edge and pulse modulation conditions. The display and data collection specifications were as specified by the Modelfest groups in the 2002 conference proceedings. To date seven subjects have participated in this phase of the data collection effort, one of which also participated in the first phase of Modelfest. Three of the spatio-temporal stimuli were identical to conditions in the original static dataset. Small differences in the thresholds were evident and may point to a stimulus limitation. The temporal CSF peaked between 4 and 8 Hz for the 0 c/d (Gaussian blob) and 4 c/d patterns. The 4 c/d and 11.3 c/d Gabor temporal CSF was low pass while the 0 c/d pattern was band pass. This preliminary expansion of the Modelfest dataset needs the participation of additional laboratories to evaluate the impact of different methods on threshold estimates and increase the subject base. We eagerly await the addition of new data from interested researchers. It remains to be seen how accurately general HVS models will predict thresholds across both Modelfest datasets.

Spatial distribution pattern is an arrangement of two or more spatial objects according to some spatial relations, such as spatial direction, topological and distance relations. In the real world, spatial objects and spatial distribution pattern all vary continuously along the time-line. Traditional spatial and non-spatial data dissevers this continuous spatio-temporal process. Under analyzing relations among spatial object, its attributes and spatial distribution pattern, we brought metaspatio- temporal process, spatio-temporal process and spatial distribution pattern spatio-temporal process. Rainfall in Eastern China has a typical spatial distribution pattern, being composed of the northern rain area and the southern rain area. Through constructing spatio-temporal process transactions, the association rules can be extracted from spatiotemporal process data set by the Apriori algorithm. The result of the spaio-temporal process association rule mining is consistent with the analysis of the theory. Finally, it is concluded that the spatio-temporal process can describe change of a spatial object in a defined time range, and change trend of one entity can be forecasted through varying trend of others based on the valuable spatio-temporal process association rules.

Blindsource separation algorithms typically involve decorrelat- ing time-aligned mixture signals. The usual assumption is that all sources are active at all times. However, if this is not the case, we show that the unique pattern of source activity\\/inactivity helps sep- aration. Music is the most obvious example of sources exhibiting repetitive structure because it is carefully constructed. We present

In an environment with multiple audio sources, blindsource separation (BSS) makes use of multiple microphone signals to estimate the respective source signals. Under normal circumstances, it is not possible to completely “unmix” the audio sources. One technique to further improve the system performance is to use all BSS outputs to generate a Wiener filter that is then applied to

In this paper, we discuss the evaluation of blind audio source separation (BASS) algorithms. Depending on the exact application, different distortions can be allowed between an estimated source and the wanted true source. We consider four dif- ferent sets of such allowed distortions, from time-invariant gains to time-varying filters. In each case, we decompose the estimated source into a true

When introducing new wastewater treatment plants (WWTP), investors and policy makers often want to know if there indeed is a beneficial effect of the installation of a WWTP on the river water quality. Such an effect can be established in time as well as in space. Since both temporal and spatial components affect the output of a monitoring network, their dependence structure has to be modelled. River water quality data typically come from a river monitoring network for which the spatial dependence structure is unidirectional. Thus the traditional spatio-temporal models are not appropriate, as they cannot take advantage of this directional information. In this paper, a state-space model is presented in which the spatial dependence of the state variable is represented by a directed acyclic graph, and the temporal dependence by a first-order autoregressive process. The state-space model is extended with a linear model for the mean to estimate the effect of the activation of a WWTP on the dissolved oxygen concentration downstream. PMID:16532730

The main goal of the project supported in this grant is to contribute to the understanding of localized spatial and spatio-temporal structures far from thermodynamic equilibrium. Here we report on our progress in the study of two classes of systems. (1) We have started to investigate localized wave-pulses in binary-mixture convection. This work is based on our recently derived extension of the conventionally used complex Ginzburg-Landau equations. We are considering three regimes: Dispersion-less supercritical waves; strongly dispersive subcritical waves; and localized waves as bound states of fronts between dispersionless subcritical waves and the motionless conductive state. (2) We have completed our investigation of steady domain structures in which domains of structures with different wave numbers alternate, separated by domain walls. In particular, we have studied their regimes of existence and stability within the framework of a Ginzburg-Landau equation and have compared it to previous results. Those were based on a long-wavelength approximation, which misses certain aspects which turn out to be important for the stability of the domain structures in realistic situations. In addition, we give a description of our work on resonantly forced waves in two-dimensional anisotropic systems.

The point reference global correlation (PRGC) technique which combines single and global measurements as proposed by Chatellier and Fitzpatrick (Exp Fluids 38(5):563-757, 2005) is of significant interest for the analysis of the turbulent statistics for noise source modeling in jet flows as it allows the 2D spatio-temporal correlation functions to be obtained over a region of the flow. This enables the statistical characteristics including inhomogeneous and anisotropic features to be determined. The sensitivity of the technique is examined in some detail for the specific case of laser doppler velocimetry (LDV) and particle image velocimetry (PIV). Simulated data are used to enable a parametric study of the accuracy of the PRGC technique to be determined as a function of the various measurement parameters. The sample frequencies and the number of samples of both the LDV and PIV signals are shown to be critical to errors associated with the estimated spatio-temporal correlations and that low data rates can lead to significant errors in the estimates. Measurements performed in single stream and co-axial jet flows at Mach 0.24 using PIV and LDV systems are reported and the 2D space-time correlation functions for these flows are determined using the PRGC technique. The results are discussed in the context of noise source modeling for jet flows.

Monitoring of the spatio-temporal variability of exposure and vulnerability indicators for risk assessments is dependent not only on the amount and quality of the data upon which the assessment is made, but also on the tools and methodologies employed to capture, store, manage and analyse the information. Spatio-temporal changes need to be properly integrated into a sound, comprehensive conceptual and methodological framework, which is able to deal with multi-dimensional data coming from different sources, at varying scales and changing over time. Commonly used approaches to capture data about an exposed building stock with respect to its physical characteristics and vulnerability usually entail a detailed (inside and outside) screening of buildings by structural engineers. These approaches are often not suitable for the rapidly changing spatio-temporal conditions in many present-day cities, and moreover do not often scale well with end-user's limited resource allocation. Also purely satellite-based approaches, which are used as time- and cost-effective alternative, show limitations in that they are only capable of providing information about vulnerability-related characteristics that can be assessed from the top view. This work, therefore, introduces a methodological and technical framework to combine remote sensing with in-situ image data capturing to overcome the limitations of previous approaches. A novel mobile mapping system and Remote Rapid Visual Screening (RRVS) technique based on omnidirectional imaging is presented. A key objective of this work is, moreover, to present a prototype spatio-temporal database system that functions as basis for the storage and management of data from different sources, at varying scales and changing over time. Examples from our study sites in Central Asia and Germany will be presented to highlight the application of the proposed approach.

Purpose: Fluoroscopic x-ray imaging systems are used extensively in spatio-temporal detection tasks and require a spatio-temporal description of system performance. No accepted metric exists that describes spatio-temporal fluoroscopic performance. The detective quantum efficiency (DQE) is a metric widely used in radiography to quantify system performance and as a surrogate measure of patient ''dose efficiency.'' It has been applied previously to fluoroscopic systems with the introduction of a temporal correction factor. However, the use of a temporally-corrected DQE does not provide system temporal information and it is only valid under specific conditions, many of which are not likely to be satisfied by suboptimal systems. The authors propose a spatio-temporal DQE that describes performance in both space and time and is applicable to all spatio-temporal quantum-based imaging systems. Methods: The authors define a spatio-temporal DQE (two spatial-frequency axes and one temporal-frequency axis) in terms of a small-signal spatio-temporal modulation transfer function (MTF) and spatio-temporal noise power spectrum (NPS). Measurements were made on an x-ray image intensifier-based bench-top system using continuous fluoroscopy with an RQA-5 beam at 3.9 {mu}R/frame and hardened 50 kVp beam (0.8 mm Cu filtration added) at 1.9 {mu}R/frame. Results: A zero-frequency DQE value of 0.64 was measured under both conditions. Nonideal performance was noted at both larger spatial and temporal frequencies; DQE values decreased by {approx}50% at the cutoff temporal frequency of 15 Hz. Conclusions: The spatio-temporal DQE enables measurements of decreased temporal system performance at larger temporal frequencies analogous to previous measurements of decreased (spatial) performance. This marks the first time that system performance and dose efficiency in both space and time have been measured on a fluoroscopic system using DQE and is the first step toward the generalized use of DQE on clinical fluoroscopic systems.

Lack of measurements is one of the main issues in hydrological modelling. However, neighbours and nested gauged catchment are precious sources of information to understand the catchment behaviours within one region. Extracting the maximum of information from those points of measurements, that could be then transposed to ungauged catchment, is still a great challenge. We propose a methodology to transpose hydrological information from gauged catchments to ungauged ones, in order to simulate streamflow hydrographs. It uses geomorphology-based hydrological modelling, which is particularly well adapted to ungauged basins thanks to its robustness, generality and flexibility. We develop a geomorphology-based model on the gauged catchment which has been built in order to capture the main behaviour of the basin. Its transfer function considers the different dynamics of the catchment through the combination of velocities and width functions. Moreover, the explicit structure of the model enables to easily create a map of isochrone areas describing the time to the outlet. Therefore, spatially distributed rainfall can then be split into those isochrone areas, permitting the transfer function to deal with spatio-temporal variability of rainfall. Once the model calibrated, using a particle swarm optimisation algorithm, its transfer function is inversed to assess the net rainfall time series. In this way, we obtained a standardized variable which is used to estimate discharge in ungauged basin. Therefore, net rainfall time series is transposed and convoluted on the ungauged catchment using its own transfer function. Spatio-temporal rainfall variability between basins is considered through a correction of this net rainfall time series. This correction is based on differences between mean gross rainfall observation among those two catchments. This methodology is applied on pairs of basins among 6 gauged basins (from 5km² to 316km²) located in Brittany, France. For the benefit of the exercise, within each pair of basins, one is considered as "gauged" and the other one as "ungauged". Different spatial configurations of pairs of basins are compared. Results demonstrates the benefit of a well defined transfer function, as well as the importance of considering rainfall variability. Finally, through the assessment of transposition efficiency, this framework is presented as an original way to describe and understand hydrological similarities in catchment behavior.

Detection of signals in noisy images is necessary in many applications, including astronomy and medical imaging. The optimal linear observer for performing a detection task, called the Hotelling observer in the medical literature, can be regarded as a generalization of the familiar prewhitening matched filter. Performance on the detection task is limited by randomness in the image data, which stems from randomness in the object, randomness in the imaging system, and randomness in the detector outputs due to photon and readout noise, and the Hotelling observer accounts for all of these effects in an optimal way. If multiple temporal frames of images are acquired, the resulting data set is a spatio-temporal random process, and the Hotelling observer becomes a spatio-temporal linear operator. This paper discusses the theory of the spatio-temporal Hotelling observer and estimation of the required spatio-temporal covariance matrices. It also presents a parallel implementation of the observer on a cluster of Sony PLAYSTATION 3 gaming consoles. As an example, we consider the use of the spatio-temporal Hotelling observer for exoplanet detection. PMID:19550494

Timbre is a major attribute of sound perception and a key feature for the identification of sound quality. Here, we present event-related brain potentials (ERPs) obtained from sixteen healthy individuals while they discriminated complex instrumental tones (piano, trumpet, and violin) or simple sine wave tones that lack the principal features of timbre. Data analysis yielded enhanced N1 and P2 responses to instrumental tones relative to sine wave tones. Furthermore, we applied an electrical brain imaging approach using low-resolution electromagnetic tomography (LORETA) to estimate the neural sources of N1/P2 responses. Separate significance tests of instrumental vs. sine wave tones for N1 and P2 revealed distinct regions as principally governing timbre perception. In an initial stage (N1), timbre perception recruits left and right (peri-)auditory fields with an activity maximum over the right posterior Sylvian fissure (SF) and the posterior cingulate (PCC) territory. In the subsequent stage (P2), we uncovered enhanced activity in the vicinity of the entire cingulate gyrus. The involvement of extra-auditory areas in timbre perception may imply the presence of a highly associative processing level which might be generally related to musical sensations and integrates widespread medial areas of the human cortex. In summary, our results demonstrate spatio-temporally distinct stages in timbre perception which not only involve bilateral parts of the peri-auditory cortex but also medially situated regions of the human brain associated with emotional and auditory imagery functions. PMID:16798014

A common goal in ecology and wildlife management is to determine the causes of variation in population dynamics over long periods of time and across large spatial scales. Many assumptions must nevertheless be overcome to make appropriate inference about spatio-temporal variation in population dynamics, such as autocorrelation among data points, excess zeros, and observation error in count data. To address these issues, many scientists and statisticians have recommended the use of Bayesian hierarchical models. Unfortunately, hierarchical statistical models remain somewhat difficult to use because of the necessary quantitative background needed to implement them, or because of the computational demands of using Markov Chain Monte Carlo algorithms to estimate parameters. Fortunately, new tools have recently been developed that make it more feasible for wildlife biologists to fit sophisticated hierarchical Bayesian models (i.e., Integrated Nested Laplace Approximation, ‘INLA’). We present a case study using two important game species in North America, the lesser and greater scaup, to demonstrate how INLA can be used to estimate the parameters in a hierarchical model that decouples observation error from process variation, and accounts for unknown sources of excess zeros as well as spatial and temporal dependence in the data. Ultimately, our goal was to make unbiased inference about spatial variation in population trends over time. PMID:23166658

Designs toward TeV-range electron-positron linear colliders include a non-zero crossing angle colliding scheme at the interaction point to mitigate instabilities and possible background. Maximizing the luminosity when operating with non-zero crossing angles requires the use of 'crab' cavities to impart a well-defined spatio-temporal correlation. In this paper we propose a novel noninterceptive diagnostic capable of measuring and monitoring the spatio-temporal correlation, i.e. the transverse position of sub-picosecond time slices, within bunch. An analysis of the proposed scheme, its spatio-temporal resolution and its limitations are quantified. Finally, the design of a proof-of-principle experiment in preparation for the Fermilab's A0 photoinjector is presented.

In this work we present a method to jointly separate active audio and visual structures on a given mixture. Blind Audiovisual Source Separation is achieved exploiting the coherence between a video sig- nal and a one-microphone audio track. The efficient representation of audio and video sequences allows to build relationships between correlated structures on both modalities. Video structures exhibiting strong

In this paper, we briefly review recent advances in blindsource separation (BSS) for nonlinear mixing models. After a general introduction to the nonlinear BSS and ICA (indepen- dent Component Analysis) problems, we discuss in more detail uniqueness issues, presenting some new results. A fundamental difficulty in the nonlinear BSS problem and even more so in the nonlinear ICA problem

We consider the problem of blind audio source separation. A method to solve this problem is blindsource separation (BSS) using independent component analysis (ICA). ICA exploits the non-Gaussianity of source in the mixtures. In this paper we propose a new wavelet based ICA method using Kurtosis for blind audio source separation. In this method, the observations are transformed into

This paper presents a Bayesian approach for blindsource recovery based on Rao-Blackwellised particle filtering techniques. The proposed state space model uses a time-varying autoregressive (TVAR) model for the sources, and a time-varying finite impulse response (FIR) model for the channel. The observed signals of the SISO, SIMO (single input, multiple output) or MIMO system are the convolution of the

The aim of this study is to investigate the behavior of radio-resistant human malignant cells, thus enabling better understanding of radiobiological effects of ions in such a case. Radiation sources such as accelerated continuous ion beams and laser technology-based ultra short radiation sources with energy of around 10 MeV will be used. The HTB140 melanoma cells are chosen since it has been shown that they represent the limit case of cellular radio-resistance among the studied tumor cell lines. These cells are particularly interesting as they provide data on the very edge of inactivation capacity of each beam line that is tested. After exposing the cell monolayers to continuous radiations of low (?-rays) and high (protons) linear energy transfer, the kinetics of disappearance of the phosphorylated histone H2AX (?-H2AX) foci per cell will be determined. The same procedure will be performed with the pulsed high dose rate protons. Detection and quantification of ?-H2AX foci will be performed by immunohistochemical 3D time-dependent imaging analyses using laser scanning confocal microscopy. Immunoblotting will enable the follow-up of the relation between ?-H2AX and cell cycle arrest via the p53/p21 pathway. In such a way the spatio-temporal changes on sub-cellular level will be visualized, quantified and compared. These results will show whether there is a difference in the effects on cells between continuous and pulsed irradiation mode. Therefore, they will contribute to the data base that might promote pulsed sources for medical treatments of malignant growths.

Moving point object data can be analyzed through the discovery of patterns. We consider the computational efficiency of detecting four such spatio-temporal patterns, namely flock, leadership, convergence, and encounter, as defined by Laube et al., 2004. These patterns are large enough subgroups of the moving point objects that exhibit similar movement in the sense of direction, heading for the same

observations of the signal in the sensor field, such as the temperature, humidity, barometric pressure, etcSpatio-Temporal Monitoring using Contours in Large-scale Wireless Sensor Networks Hadi Alasti and time using large scale wireless sensor networks. The proposed algorithms use contours for estimating

Retinal models based on the cellular neural network (CNN) paradigm have been widely used. These neuromorphic models are based on retinal anatomy and physiology. In this paper a framework is proposed for qualitative spatio-temporal studies in vertebrate retinas, the underlying retinal anatomy is followed as closely as possible, the characteristics of the physiological models, however, are kept simple. The goal

SPATIO-TEMPORAL REGRESSION MODELS FOR DEFORESTATION IN THE BRAZILIAN AMAZON Giovana M. de change, spatial simultaneous autoregression ABSTRACT: Deforestation in the Brazilian Amazon has sharply of deforestation in a selected area by relating data from 2002-2008 to a number of explanatory variables, part

Understanding Spatio-Temporal Uncertainty in Medium Access with ALOHA Protocols Affan Syed, Wei Ye-dependent propagation latency affects medium access control (MAC) by using ALOHA as a case study. MAC protocols in un of synchronization in slotted ALOHA is com- pletely lost due to such latency. To handle spatial uncer- tainty, we

, integrated into a head-mounted display (HMD), could use computer vision techniques to enhance human visual of a system that implements our gaze-contingent spatio-temporal filtering algorithm in an HMD with video-see-through. Subjects will be able to walk around, seeing their natural visual environment inside the HMD. We

Selecting Ghosts and Queues from a Car Trackers Output using a Spatio-Temporal Query Language of a tracker working on a car traffic scene. The results of two example sets of queries are shown in two videos shows how to find queues of cars in the road traffic scene without prior knowledge of lanes. 1

Forecasts of wind power production are increasingly being used in various management tasks. So far, such forecasts and related uncertainty information have usually been generated individuallyfor a given site of interest (either a wind farm or a group of wind farms), without properly accounting for the spatio- temporal dependencies observed in the wind generation field. However, it is intuitively expected

Spatio-Temporal Dynamics of Online Memes: A Study of Geo-Tagged Tweets Krishna Y. Kamath, James of hashtags, which is important for understanding meme diffusion and infor- mation propagation; (ii) examine this content, some content may gain traction and be- come popular resulting in viral videos and popular memes

Spatio-Temporal Meme Prediction: Learning What Hashtags Will Be Popular Where Krishna Y. Kamath of predicting what online memes will be popular in what locations. Specif- ically, we develop data on the problem of predicting what online memes will be popular in what locations, which has Permission to make

The maximization of spatio-temporal stochastic interactions (called TIM) has been proposed as an information-theoretic organizing principle in neural systems which supports a high cooperativity among cells and complex correlation patterns. The present work shows that temporal learning rules induce a high (though not always maximal) stochastic interaction in Markov chains and probabilistic neural networks.

BORA: Routing and Aggregation for Distributed Processing of Spatio-Temporal Range Queries Goce) capabilities. The MOD server stores the data for the moving objects in a given cell, pro- cesses the continuous-temporal data and processing various (continuous) queries, however, a typical assumption in most of them

movements. An interest- ing problem is to find dense clusters of objects which move similarly for a longOn Discovering Moving Clusters in Spatio-temporal Data Panos Kalnis1 , Nikos Mamoulis2, Hong Kong,sbakiras@cs.ust.hk Abstract. A moving cluster is defined by a set of objects that move close

A SPATIO-TEMPORAL GENERALIZED FOURIER DOMAIN FRAMEWORK TO ACOUSTIC MODELING IN ENCLOSED SPACES thousands of acoustic input/output channels [2]. It is clear that to enable real- time, full for multichannel acoustical modeling in enclosed spaces. In Fourier analysis, it is well known that sampling in one

This paper summarizes recent developments in the measurement and analysis of surface EMG in cases where a large number of skin electrodes allows a spatio-temporal view on the EMG signal characteristics. The technical possibilities and different goals of using these topographical EMG techniques are outlined

This study examines the spatio-temporal structure of diatom assemblages in a temperate estuary (Ria de Aveiro, Western Portugal). Eighteen monthly surveys were conducted, from January 2002 to June 2003, at three sampling sites (at both high and low tide) along the estuarine salinity gradient. The relationship of diatom assemblages and environmental variables was analysed using the STATICO method, which has

The Latent Heat (LH), released as a result of deep convection, plays an important role in the vertical distribution of the diabatic energy budget from the surface to the atmosphere: the motor which drives the global atmospheric circulation, including the Southeast Asian Monsoon. In particular, knowing the spatio-temporal structure of the LH during the wet monsoon season could be a

The latent heat released as a result of deep convection plays an important role driving the global atmospheric circulation, including the Southeast Asian Monsoon. In particular, knowing the spatio-temporal structure of the latent heating during the wet monsoon season its very important to understand the interaction between the seasonal features of the monsoon and the summer manifestation of the intraseasonal

Spatio-Temporal Continuous Wavelet Transforms for Motion-Based Segmentation in Real Image Sequences- based segmentation for digital image sequences that is based on continuous wavelet transform. Continu of the squared modulus of the continuous wavelet transforms. A clustering process is then necessary to perform

BACKGROUND: Epidemiologic studies are often confounded by the human and environmental interactions that are complex and dynamic spatio-temporal processes. Hence, it is difficult to discover nuances in the data and generate pertinent hypotheses. Dynamic mapping, a method to simultaneously visualize temporal and spatial information, was introduced to elucidate such complexities. A conceptual framework for dynamic mapping regarding principles and implementation

A Dense SURF and Triangulation based Spatio-Temporal Feature for Action Recognition Do Hang Nga frame set, we first extract dense SURF keypoints from its first frame. We then select points are needed to repre- sent actions, while only spatio features such as SIFT and SURF are needed for object

to reduce aliasing artifacts and noise. The reference is created from undersampled dynamic data by combining-space and requires special techniques for reconstruction. Even if undersampling artifacts are removed, sharpness motion. The proposed method was compared to reconstruction using the spatio-temporal constrained

Mapping Ancient Forests: Bayesian Inference for Spatio-Temporal Trends in Forest Composition Using the Fossil Pollen Proxy Record Christopher J. PACIOREK and Jason S. MCLACHLAN Ecologists use the relative abundance of fossil pollen in sediments to estimate how tree species abundances change over space and time

a network of CCTV cameras. 1 Introduction Events over time may be described using a spatio-temporal data of CCTV cameras. Both of these approaches benefit from breaking the solution down into its component parts-model for each of the outcomes in the game. In the CCTV scenario this means learning the actions of single

of forest dynamics and their integration in models of forest dynamics, (2) strengths and limitations-based methods in the analysis of forest dynamics. These topics are discussed with reference to their potential for solving open questions in the studies of forest dynamics. The study of spatio-temporal processes provides

14 STARR-DCS: Spatio-Temporal Adaptation of Random Replication for Data-Centric Storage ´ANGEL-centric storage (DCS) in a wireless sensor and actor network (WSAN) that employs a randomly selected set of data burdens by varying their locations. To that end, we propose and validate a simple model to determine

A STARR-DCS: Spatio-Temporal Adaptation of Random Replication for Data Centric Storage ´ANGEL presents a novel framework for Data Centric Storage in a Wireless Sensor and Actor Network (WSAN we propose and validate a simple model to determine the optimal number of replicas, in terms

This paper addresses the problems of state space reconstruction and spatio-temporal prediction for lattice dynamical systems. It is shown that the state space of any finite lattice dynamical system can be embedded into a reconstruction space for almost every, in the sense of prevalence, smooth measurement mapping as long as the dimension of the reconstruction space is larger than twice

Spatio-temporal delays in a nutrient-plankton model on a finite domain: linear stability of a finite, one-dimen- sional domain. To illustrate the ideas we concentrate on a diffusive nutrient-plankton modelling the evolution of a plankton population feeding on nutrient that is supplied at a constant rate

SPATIO-TEMPORAL DISTRIBUTION OF PLASMODIUM FALCIPARUM AND P. VIVAX MALARIA IN THAILAND GUOFA ZHOU were analyzed to determine the spatial and temporal dynamics of Plasmodium falciparum and P. vivax of malaria cases are caused by Plasmodium falciparum and P. vivax infections. Since the appearance

Computing Spatio­Temporal Representations of Human Faces Yaser Yacoob & Larry Davis Computer Vision at its peak [15]. These pic­ tures allow one to detect the presence of static cues (e.g., wrinkles. Bassili [2] suggested that motion in the image of a face would allow emotions to be identified even

. This policy applies also to disabled persons. The RTG will take measures to support the research carrier://graphmod.iwr.uni-heidelberg.de/Members.10.0.html. The Research Training Group is run by a consortium from the Institutes of AppliedResearch Training Group (Graduiertenkolleg) 1653 Spatio/Temporal Probabilistic Graphical Models

propose a spatio-temporal access control model, based on the Role-Based Access Control (RBAC) model, that is suitable for pervasive computing applications. We show the association of each component of RBAC) or Role- Based Access Control (RBAC) do not take into account such environmental factors in determining

In this study we describe an ambulatory system for estimation of spatio-temporal parameters during long periods of walking. This original method based on wavelet analysis is proposed to compute the values of temporal gait parameters from the angular velocity of lower limbs. Based on a mechanical model, the medio-lateral rotation of the lower limbs during stance and swing, the stride

The Model Web envisions an infrastructure for coupling environmental models in the Web. In environmental sciences, the phenomena of interest are usually not well-bounded objects, but rather continuous phenomena in space and time. These phenomena are commonly referred to as spatial or spatio-temporal fields and are often modelled as random variables. Currently, spatio-temporal fields are usually represented and exchanged as raster data. Besides the communication overhead this imposes, exchanging rasters has also other drawbacks. For example, the interpolation method used to calculate the raster values as well as the original observations the raster originates from are usually not part of the resulting data. Furthermore, the interpolated values are commonly single moment estimates of the random variables such as their expectation values. Thus, the natural randomness in the interpolated variables and interpolation uncertainties are also not available any more after interpolation. We propose a new model for exchanging spatio-temporal random fields as the original sample data plus information about the model of spatial or spatio-temporal variance describing the random field. This allows to communicate the complete random variables and their associated uncertainties opposed to single estimates. In addition, this approach suggests a particular interpolation method to calculate rasters from the field. The desired raster resolution and projection can then be chosen by the user of the field data. This is advantageous to the classical approach, as transformations between coordinate reference systems typically distort the given raster and changing the raster's resolution usually imposes a second model assumption on the interpolated field data. Using a standardized language to describe spatio-temporal random fields allows for a fully machine readable approach. Depending on the target application, one can thus easily obtain one to several simulations of the field reflecting its random nature instead of a single interpolated grid based on a single moment estimate of the underlying distribution with a fixed resolution and coordinate reference system. We present an extension of FieldGML, a language based on the Geography Markup Language (GML) for representing spatial fields, and UncertML, the Uncertainty Markup Language, where spatial and spatio-temporal variance is described by means of the kriging procedure assuming underlying Gaussian distributions. Prototypical implementations are provided for web-processes within the UncertWeb project and as a R package building on top of the widely used packages sp, gstat and spacetime for spatial and spatio-temporal random fields.

A new method for blindsource separation of instantaneous mixtures is developed. It exploits both the spectral and time diversity of the sources and is based on Gaussian mutual information. As a result, it uses only second order statistics and can be efficiently implemented through a joint diagonalization algorithm. Simulation results illustrate the good performance of the method.

Colorectal cancer usually appears in polyps developed from the mucosa. Carcinoma is frequently found in those polyps larger than 10mm and therefore only this kind of polyps is sent for pathology examination. In consequence, accurate estimation of a polyp size determines the surveillance interval after polypectomy. The follow up consists in a periodic colonoscopy whose frequency depends on the estimation of the size polyp. Typically, this polyp measure is achieved by examining the lesion with a calibrated endoscopy tool. However, measurement is very challenging because it must be performed during a procedure subjected to a complex mix of noise sources, namely anatomical variability, drastic illumination changes and abrupt camera movements. This work introduces a semi-automatic method that estimates a polyp size by propagating an initial manual delineation in a single frame to the whole video sequence using a spatio-temporal characterization of the lesion, during a routine endoscopic examination. The proposed approach achieved a Dice Score of 0.7 in real endoscopy video-sequences, when comparing with an expert. In addition, the method obtained a root mean square error (RMSE) of 0.87mm in videos artificially captured in a cylindric structure with spheres of known size that simulated the polyps. Finally, in real endoscopy sequences, the diameter estimation was compared with measures obtained by a group of four experts with similar experience, obtaining a RMSE of 4.7mm for a set of polyps measuring from 5 to 20mm. An ANOVA test performed for the five groups of measurements (four experts and the method) showed no significant differences (p<0.01). PMID:25670148

is the acoustic blindsource separation problem in which sound sources are mixed in a reverberant environment or larger swath. Other processes detectable with cICA might be spatially fluid non-brain processes, for 2

Understanding the spatio-temporal dynamics of insects in agroecosystems is crucial when developing effective management strategies that emphasise biological control of pests. Wild populations of Trichogramma Westwood egg parasitoids are utilised for biological suppression of the potentially resistan...

Carbon isotope ratios (13C/12C) can be used to trace sources of production supporting food chains, as ?13C undergoes relatively small and predictable increases (?0.5‰) through each trophic level. However, for this technique to be precise, variation in ?13C signatures of different sources of production (baseline sources) must be clearly defined and distinct from each other. Despite this, ?13C in the primary producers of marine systems are highly variable over space and time, due to the complexity of physical and biogeochemical processes that drive ?13C variation at the base of these foodwebs. We measured spatial and temporal variation in the ?13C of two species of macroalgae that are important dietary components of grazers over temperate reefs: the small kelp Ecklonia radiata, and the red alga Plocamium preissianum, and related any variation to a suite of physical and biogeochemical variables. Patterns in ?13C variation, over different spatial (10 s m to 100 km) and temporal scales (weeks to seasons), differed greatly between taxa, but these were partly explained by the ?13C of dissolved inorganic carbon (DIC) and light. However, while the ?13C in E. radiata was not related to water temperature, a highly significant proportion of the spatio-temporal variation in ?13C of P. preissianum was explained by temperature alone. Accordingly, we applied this relationship to project (across temperate Australasia) and forecast (in time, south-western Australia) patterns in P. preissianum ?13C. The mean projected ?13C for P. preissianum in the study region varied by only ?1‰ over a 12-month period, compared to ?3‰ over 2000 km. This illustrates the potential scale in the shift of ?13C in baseline food sources over broad scales, and its implications to food web studies. While we show that those relationships differ across taxonomic groups, we recommend developing models to explain variability in ?13C of other baseline sources to facilitate the interpretation of variation in ?13C of consumers in food webs, particularly where data for baselines are absent over broad scales.

Studies of food webs often employ stable isotopic approaches to infer trophic position and interaction strength without consideration\\u000a of spatio-temporal variation in resource assimilation by constituent species. Using results from laboratory diet manipulations\\u000a and monthly sampling of field populations, we illustrate how nitrogen isotopes may be used to quantify spatio-temporal variation\\u000a in resource assimilation in ants. First, we determined nitrogen

This study reports the spatio-temporal changes in water quality of Nullah Aik, tributary of the Chenab River, Pakistan. Stream\\u000a water samples were collected at seven sampling sites on seasonal basis from September 2004 to April 2006 and were analyzed\\u000a for 24 water quality parameters. Most significant parameters which contributed in spatio-temporal variations were assessed\\u000a by statistical techniques such as Hierarchical

In this paper, we propose a novel mechanism for spectrum sensing that leads us to exploit the spatio-temporal correlation present in the received signal at a multi-antenna receiver. For the proposed mechanism, we formulate the spectrum sensing scheme by adopting the generalized likelihood ratio test (GLRT). However, the GLRT degenerates in the case of limited sample support. To circumvent this problem, several extensions are proposed that bring robustness to the GLRT in the case of high dimensionality and small sample size. In order to achieve these sample-efficient detection schemes, we modify the GLRT-based detector by exploiting the covariance structure and factoring the large spatio-temporal covariance matrix into spatial and temporal covariance matrices. The performance of the proposed detectors is evaluated by means of numerical simulations, showing important advantages over existing detectors.

A spatio-temporal model of housing price trends is developed that focuses on individual housing sales over time. The model allows for both the spatio-temporal lag effects of previous sales in the vicinity of each housing sale, and for general autocorrelation effects over time. A key feature of this model is the recognition of the unequal spacing between individual housing sales over time. Hence the residuals are modeled as a first-order autoregressive process with unequally spaced events. The maximum-likelihood estimation of this model is developed in detail, and tested in terms of simulations based on selected data. In addition, the model is applied to a small data set in the Philadelphia area.

Time and location data play a very significant role in a variety of factory automation scenarios, such as automated vehicles and robots, their navigation, tracking, and monitoring, to services of optimization and security. In addition, pervasive wireless capabilities combined with time and location information are enabling new applications in areas such as transportation systems, health care, elder care, military, emergency response, critical infrastructure, and law enforcement. A person/object in proximity to certain areas for specific durations of time may pose a risk hazard either to themselves, others, or the environment. This paper presents a novel fuzzy based spatio-temporal risk calculation DSTiPE method that an object with wireless communications presents to the environment. The presented Matlab based application for fuzzy spatio-temporal risk cluster extraction is verified on a diagonal vehicle movement example.

We consider the application of KronPCA spatio-temporal modeling techniques1, 2 to the extraction of spatiotemporal features for video dismount classification. KronPCA performs a low-rank type of dimensionality reduction that is adapted to spatio-temporal data and is characterized by the T frame multiframe mean ? and covariance ? of p spatial features. For further regularization and improved inverse estimation, we also use the diagonally corrected KronPCA shrinkage methods we presented in.1 We apply this very general method to the modeling of the multivariate temporal behavior of HOG features extracted from pedestrian bounding boxes in video, with gender classification in a challenging dataset chosen as a specific application. The learned covariances for each class are used to extract spatiotemporal features which are then classified, achieving competitive classification performance.

Human action recognition has drawn much attention in the field of video analysis. In this paper, we develop a human action detection and recognition process based on the tracking of Interest Points (IP) trajectory. A pre-processing step that performs spatio-temporal action detection is proposed. This step uses optical flow along with dense speed-up-robust-features (SURF) in order to detect and track moving humans in moving fields of view. The video description step is based on a fusion process that combines displacement and spatio-temporal descriptors. Experiments are carried out on the big data-set UCF-101. Experimental results reveal that the proposed techniques achieve better performances compared to many existing state-of-the-art action recognition approaches.

Einstein-Podolsky-Rosen (EPR) steering is a type of quantum correlation which allows one to remotely prepare, or steer, the state of a distant quantum system. While EPR steering can be thought of as a purely spatial correlation there does exist a temporal analogue, in the form of single-system temporal steering. However, a precise quantification of such temporal steering has been lacking. Here we show that it can be measured, via semidefinite programming, with a temporal steerable weight, in direct analogy to the recently proposed EPR steerable weight. We find a unique application for both, this and a spatio-temporal extension, by showing that they are monotonically-decreasing functions under completely-positive trace-preserving maps and thus can be used to define a practical measure of strong non-Markovianity. Finally, we discuss how a spatio-temporal steerable weight can be used to check whether two nodes of a quantum network are quantum connected.

Blindsource separation consists of recovering a set of signals of which only instantaneous linear mixtures are observed. Thus far, this problem has been solved using statistical information available on the source signals. This paper introduces a new blindsource separation approach exploiting the difference in the time-frequency (t-f) signatures of the sources to be separated. The approach is based

Convolutive BlindSource Separation based on Multiple Decorrelation. Lucas Parra, Clay Spence, Bert of researchers have published in recent years on the prob- lem of blindsource separation. For one, the problem of di erently convolved sources. The task of source separation is to identify the multiple channels

This paper presents a novel approach for representing the spatio-temporal topology of the camera network with overlapping and non-overlapping fields of view (FOVs). The topology is determined by tracking moving objects and establishing object correspondence across multiple cameras. To track people successfully in multiple camera views, we used the Merge-Split (MS) approach for object occlusion in a single camera and

Nonquadratic variational regularization is a well-known and powerful approach for the discontinuity-preserving computation of optic flow. In the present paper, we consider an extension of flow-driven spatial smoothness terms to spatio-temporal regularizers. Our method leads to a rotationally invariant and time symmetric convex optimization problem. It has a unique minimum that can be found in a stable way by standard

Spatio-temporal salient features are widely being used for compact representation of objects and motions in video, especially for event and action recognition. The existing feature extraction methods have two main problems: First, they work in batch mode and mostly use Gaussian (linear) scale-space filtering for multi-scale feature extraction. This linear filtering causes the blurring of the edges and salient motions

In this work, we present a novel local descriptor for video sequences. The proposed descriptor is based on histograms of oriented 3D spatio-temporal gradients. Our contribution is four-fold. (i) To compute 3D gradients for arbitrary scales, we develop a memory-efficient algorithm based on integral videos. (ii) We propose a generic 3D orientation quantization which is based on regular polyhedrons. (iii)

This paper presents an approach to learning an optimal behavioral parameterization in the framework of a Case-Based Reasoning methodology for autonomous navigation tasks. It i s based on our previous work on a behavior-based robotic system that also employed spatio-temporal case-based reasoning (3) in the selection of behavioral parameters but was not capable of learning new parameterizations. The present method

This paper extends 2D Active Shape Models to 2D+time by presenting a method for modelling and segmenting spatio-temporal shapes (ST-shapes). The modelling part consists of constructing a statistical model of ST-shape parameters. The model obtained describes the principal modes of variation of the ST-shape in addition to certain constraints on the allowed variations. An active approach is used in segmentation;

The Spatio-Temporal Analysis of Field Fluctuations (STAFF) experiment is one of five experiments which together comprise the Wave Experiment Consortium (WEC). STAFF consists of a three-axis search coil magnetometer to measure magnetic fluctuations at frequencies up to 4 kHz, and a spectrum analyser to calculate in near-real time aboard the spacecraft, the complete auto- and cross-spectral matrices using the three

the paper introduces a cognitively motivated approach for structuring and representing of unfamiliar large-scale environments. The proposed region- based representation facilitates collaborative spatio-temporal planning, where problem solving process is shared between a user and an assistance system. The problem domain is structured hierarchically into regions resembling human decision space. The region-based structure makes it possible to specify spatial constraints as

This paper studies dynamical properties of spatio-temporal pattern sequences ("synfirechains") in associative networks of spiking neurons. Employing postsynaptic potentialswith a finite rise-time, the replay speed of stored sequences can be controledby unspecific background signals. In addition, the speed also depends on the numberof co-activated sequences, but balanced inhibition can prevent this dependency.An implicit equation is derived and solved numerically which

Spatio-temporal patterns and driving mechanisms of forest carbon dioxide (CO2) exchange are the key issues on terrestrial ecosystem carbon cycles, which are the basis for developing and validating ecosystem\\u000a carbon cycle models, assessing and predicting the role of forests in global carbon balance. Eddy covariance (EC) technique,\\u000a an important method for measuring energy and material exchanges between terrestrial ecosystems and

Recently, rapid and non-destructive magnetic measurements have been increasingly used as a proxy method for the assessment of heavy metal pollution in urban areas. The spatio-temporal variations of anthropogenic particulate matter in roadside dust of the Seoul metropolitan area have been investigated using 1,353 dust samples collected monthly from 33 locations during 13 months period (February 2002 through February 2003).

The technique of spatially resolved cross-correlation spectroscopy (CCS) is used to carry out diagnostic measurements of the barrier discharge (BD) in air at atmospheric pressure. Quantitative estimates for electric field strength E(x,t) and for relative electron density ne(x,t)\\/nemax are derived from the experimentally determined spatio-temporal distributions of the luminosity for the spectral bands of the 0-0 transitions of the second

We have developed a method that can remove view-disturbing noise from image sequences by spatio-temporal image processing. In outdoor environments on rainy days, pictures taken by a camera are often degraded because of adherent noise, such as water drops on the surface of the lens-protecting glass of the camera. To solve this problem, our method uses image sequences captured with

In order to study the spatio-temporal characteristics of human lower limbs at different walking speeds, VICON MX three-dimensional motion capturing system was adopted to measure the three dimensional motion trajectories of lower limbs when human were walking on the treadmill. Through Matlab and Origin Pro7.5, the joint angles of lower limbs were calculated and gait cycles were divided. The results

We review the spatio-temporal dynamical features of the Ananthakrishna model for the Portevin-Le Chatelier effect, a kind of plastic instability observed under constant strain rate deformation conditions. We then establish a qualitative correspondence between the spatio-temporal structures that evolve continuously in the instability domain and the nature of the irregularity of the scalar stress signal. Rest of the study is on quantifying the dynamical information contained in the stress signals about the spatio-temporal dynamics of the model. We show that at low applied strain rates, there is a one-to-one correspondence with the randomly nucleated isolated bursts of mobile dislocation density and the stress drops. We then show that the model equations are spatio-temporally chaotic by demonstrating the number of positive Lyapunov exponents and Lyapunov dimension scale with the system size at low and high strain rates. Using a modified algorithm for calculating correlation dimension density, we show that the stress-strain signals at low applied strain rates corresponding to spatially uncorrelated dislocation bands exhibit features of low dimensional chaos. This is made quantitative by demonstrating that the model equations can be approximately reduced to space independent model equations for the average dislocation densities, which is known to be low-dimensionally chaotic. However, the scaling regime for the correlation dimension shrinks with increasing applied strain rate due to increasing propensity for propagation of the dislocation bands. The stress signals in the partially propagating to fully propagating bands turn to have features of extensive chaos.

It is critical to detect the spatio-temporal conflicts in a project schedule, since many construction conflicts occur due to constraints in construction space and unavailability of intermediate functions of the in-progress building. This paper introduces a temporal 3D space system modelling method using a COmponent State network CEntric Model (COSCEM) to integrate such project aspects as product, process, space, and

We explored the spatio-temporal dynamics of odor-evoked activity in the rat and mouse main olfactory bulb (MOB) using voltage-sensitive dye imaging (VSDI) with a new probe. The high temporal resolution of VSDI revealed odor-specific sequences of glomerular activation. Increasing odor concentrations reduced response latencies, increased response amplitudes, and recruited new glomerular units. However, the sequence of glomerular activation was maintained.

Spatio-temporal statistical models are increasingly being used across a wide variety of scientific disciplines to describe\\u000a and predict spatially-explicit processes that evolve over time. Correspondingly, in recent years there has been a significant\\u000a amount of research on new statistical methodology for such models. Although descriptive models that approach the problem from\\u000a the second-order (covariance) perspective are important, and innovative work

Marine vertebrate strandings data can provide insights into the long-term dynamics of cetacean populations, and the threats\\u000a they face. We investigate whether the spatio-temporal patterns of cetacean strandings around Cornwall, SW Britain, have changed\\u000a in the past century. Analysis of strandings from 1911 to 2006 (n = 2,257) show that, since the mid-1970s, the relative frequency of strandings of common dolphins (Delphinus

Marine vertebrate strandings data can provide insights into the long-term dynamics of cetacean populations, and the threats they face. We investigate whether the spatio-temporal patterns of cetacean strandings around Cornwall, SW Britain, have changed in the past century. Analysis of strandings from 1911 to 2006 (n = 2,257) show that, since the mid-1970s, the relative frequency of strandings of common

Activity-dependent plasticity probably plays a key role in learning and memory in biological information processing systems. Though long-term potentiation and depression have been extensively studied in the filed of neuroscience, little is known on the mechanisms for integrating these modifications on network-wide activity changes. In this report, we studied effects of spatio-temporally correlated stimuli on the neuronal network activity. Rat

In this contribution we describe a vision system for model- based 3D detection and spatio-temporal pose estimation of objects in cluttered scenes. As low-level features, our approach requires 3D depth points along with information about their motion and the direction of the local intensity gradient. We extract these features by spacetime stereo based on local image intensity modelling. After applying

Databases such as Oracle, IBM, SQLServer are increasing their support for temporal and spatio- temporal data with every release. In this paper, we describe how Oracle users can extend the existing functionality in Oracle and ground their research by using Oracle's extensibility. To illustrate its applicability to temporal data, we present how a Map21 (M.A. Nascimento and M.H. Dunham, 1999)

Traditionally, dynamic PET studies reconstruct temporally contiguous PET images using algorithms which ignore the inherent consistency between frames. We present a method which imposes a regularisation constraint based on wavelet denoising. This is achieved efficiently using the Dual Tree Complex Wavelet Transform (DT-CWT) of Kingsbury, which has many important advantages over the traditional discrete wavelet transform: shift invariance, implicit measure of local phase, and directional selectivity. In this paper, we apply the decomposition to the full spatio-temporal volume and use it for the reconstruction of dynamic (spatio-temporal) PET data. Instead of using traditional wavelet thresholding schemes we introduce a locally defined and empirically-determined Cross Scale regularisation technique. We show that wavelet based regularisation has the potential to produce superior reconstructions and examine the effect various levels of boundary enhancement have on the overall images. We demonstrate that wavelet-based spatio-temporally regularised reconstructions have superior performance over conventional Gaussian smoothing in simulated and clinical experiments. We find that our method outperforms conventional methods in terms of signal-to-noise ratio (SNR) and Mean Square Error (MSE), and removes the need to post-smooth the reconstruction. PMID:20426137

Spatial planning is a crucial area for balancing civilization development with environmental protection. Spatial planning has a multidisciplinary nature. It must take into account the dynamics of the processes, which could affect the integrity of the environmental system. That is why we need a new approach to modelling phenomena occurring in space. Such approach is offered by ontologies, based on Description Logic (DL) and related to inference systems. Ontology is a system for the knowledge representation, including conceptual scheme and based on this scheme representation of reality. Ontologies can be enriched with additional logical systems. The authors present a way of building domain ontologies for spatial planning, including the representation of spatio-temporal phenomena. Description Logic is supplemented by structures of temporal logic. As a result, the analysis for exploring the topological relations between spatial objects will be extended to include temporal relationships: coincidence, precedence and succession, cause and effect relationship. Spatio-temporal models with temporal logic structures, encoded in ontologies, could be a subject of inference process, performed by semantic reasoners (reasoner engines). Spatio-temporal representations are offered, by so-called upper ontologies, such as GFO, BFO, OCHRE and others. Temporal structures provided in such ontologies, are useful for the analysis of data obtained from environmental and development monitoring systems and for description and representation of historical phenomena. They allow creating the models and scenarios of expected spatial transformation. They will support analysis for spatial development design, decision-making in spatial planning and forecasting of environmental impact.

Odor processing in the animal olfactory system is still an open problem in modern neuroscience. It is a common understanding that the spatial code provided by the activity distribution of the olfactory receptor cells (ORC) due the presence of an odorant is transformed into a spatio-temporal code in the mitral cell (MC) layer in the case of mammals, or the projection neurons (PN) in the case of insects, that is decoded later along the neural path. The putative role of the spatio-temporal coding is to disambiguate the stimulus putting it in a more robust representation that allows odor separation, categorization, and recognition. Oscillations due to lateral inhibition among MC's (or PN's) may play an important part in the code as well as neural adaptation. To shed some light on their possible role in the olfaction processing, we study the properties of a simple network model. Upon the presentation of a random distributed input it respond with a rich spatio-temporal structure where two distinct phases are observed. We discuss their properties and implications in information processing.

In many biological systems, variability of the components can be expected to outrank statistical fluctuations in the shaping of self-organized patterns. In pioneering work in the late 1990s, it was hypothesized that a drift of cellular parameters (along a ‘developmental path’), together with differences in cell properties (‘desynchronization’ of cells on the developmental path) can establish self-organized spatio-temporal patterns (in their example, spiral waves of cAMP in a colony of Dictyostelium discoideum cells) starting from a homogeneous state. Here, we embed a generic model of an excitable medium, a lattice of diffusively coupled FitzHugh–Nagumo oscillators, into a developmental-path framework. In this minimal model of spiral wave generation, we can now study the predictability of spatio-temporal patterns from cell properties as a function of desynchronization (or ‘spread’) of cells along the developmental path and the drift speed of cell properties on the path. As a function of drift speed and desynchronization, we observe systematically different routes towards fully established patterns, as well as strikingly different correlations between cell properties and pattern features. We show that the predictability of spatio-temporal patterns from cell properties contains important information on the pattern formation process as well as on the underlying dynamical system. PMID:23349439

Recent advances in the study of the characteristics, processes, and causes of spatio-temporal variabilities of the East Asian monsoon (EAM) system are reviewed in this paper. The understanding of the EAM system has improved in many aspects: the basic characteristics of horizontal and vertical structures, the annual cycle of the East Asian summer monsoon (EASM) system and the East Asian winter monsoon (EAWM) system, the characteristics of the spatio-temporal variabilities of the EASM system and the EAWM system, and especially the multiple modes of the EAM system and their spatio-temporal variabilities. Some new results have also been achieved in understanding the atmosphere-ocean interaction and atmosphere-land interaction processes that affect the variability of the EAM system. Based on recent studies, the EAM system can be seen as more than a circulation system, it can be viewed as an atmosphere-ocean-land coupled system, namely, the EAM climate system. In addition, further progress has been made in diagnosing the internal physical mechanisms of EAM climate system variability, especially regarding the characteristics and properties of the East Asia-Pacific (EAP) teleconnection over East Asia and the North Pacific, the "Silk Road" teleconnection along the westerly jet stream in the upper troposphere over the Asian continent, and the dynamical effects of quasi-stationary planetary wave activity on EAM system variability. At the end of the paper, some scientific problems regarding understanding the EAM system variability are proposed for further study.

While many techniques exist for local spectrum sensing of a primary user, each represents a computationally demanding task to secondary user receivers. In software-defined radio, computational complexity lengthens the time for a cognitive radio to recognize changes in the transmission environment. This complexity is even more significant for spatially multiplexed receivers, e.g., in SIMO and MIMO, where the spatio-temporal data sets grow in size with the number of antennae. Limits on power and space for the processor hardware further constrain SDR performance. In this report, we discuss improvements in spatio-temporal twice whitening (STTW) for real-time local spectrum sensing by demonstrating a form of STTW well suited for MIMO environments. We implement STTW on the Coherent Logix hx3100 processor, a multicore processor intended for low-power, high-throughput software-defined signal processing. These results demonstrate how coupling the novel capabilities of emerging multicore processors with algorithmic advances can enable real-time, software-defined processing of large spatio-temporal data sets.

Radioactive particle movement analysis in any environment gives valuable information about the effects of the concerned environment on the particle and the transportation phenomenon. In this study, the spatio-temporal point cumulative semivariogram (STPCSV) approach is proposed for the analysis of the spatio-temporal changes in the radioactive particle movement within a surface water body. This methodology is applied to the (210)Pb radioactive isotope measurements at 44 stations, which are determined beforehand in order to characterize the Keban Dam water environment on the Euphrates River in the southeastern part of Turkey. It considers the contributions coming from all the stations and provides information about the spatio-temporal behavior of (210)Pb in the water environment. After having identified the radii of influences at each station it is possible to draw maps for further interpretations. In order to see holistically the spatial changes of the radioisotope after 1st, 3rd and 5th hours, the radius of influence maps are prepared and interpreted accordingly. PMID:19027230

The distribution of potential evaporation is highly unstable due to complex human activities and climate changes. Therefore, it is of great significance for further understanding hydrological cycle to estimate potential evaporation distribution. Reasonable regionalization of potential evaporation will help to improve the efficiency of irrigation and increase the ability of drought relief, which is of great importance to irrigation planning and management. Hence, the spatio-temporal changes in potential evaporation distribution at monthly and annual scales are investigated based on the modified Mann-Kendall trend test method and the entropy theory in the Wei River Basin. A nonparametric method as an attractive alternative to empirical and parametric approaches is proposed to calculate the univariate and bivariate probability distribution of potential evaporation. The directional information transfer index (DITI) is employed to estimate the similarity among the meteorological stations, and the k-means cluster analysis is used to classify the meteorological stations into several distribution zones with distinct features. Based on the monthly potential evaporation from 1960 to 2008 at 21 meteorological stations, the basin is ultimately classified into 8 zones with their own distinct spatio-temporal distribution features. In view of the distinct spatio-temporal distribution features, the DITI-based model combined with the nonparametric probability estimation method and the k-means cluster analysis offers a more precise classification of potential evaporation distribution zones.

The restrictions of the analysis of natural processes which are observed at any point in space or time to a purely spatial or purely temporal domain may cause loss of information and larger prediction errors. Moreover, the arbitrary combinations of purely spatial and purely temporal models may not yield valid models for the space-time domain. For such processes the variation can be characterized by sophisticated spatio-temporal modeling. In the present study the composite spatio-temporal Bayesian maximum entropy (BME) method and transformed hierarchical Bayesian space-time interpolation are used in order to predict precipitation in Pakistan during the monsoon period. Monthly average precipitation data whose time domain is the monsoon period for the years 1974-2000 and whose spatial domain are various regions in Pakistan are considered. The prediction of space-time precipitation is applicable in many sectors of industry and economy in Pakistan especially; the agricultural sector. Mean field maps and prediction error maps for both methods are estimated and compared. In this paper it is shown that the transformed hierarchical Bayesian model is providing more accuracy and lower prediction error compared to the spatio-temporal Bayesian maximum entropy method; additionally, the transformed hierarchical Bayesian model also provides predictive distributions.

Central and Eastern Europe are prone to severe floods due to heavy rainfall that cause societal and economic damages, ranging from agriculture to water resources, from the insurance/reinsurance sector to the energy industry. To improve the flood risk analysis, a better characterisation and modelling of the rainfall patterns over this area, which involves the Danube river watershed, is strategically important. In this study, we analyse the spatio-temporal properties of a large data set of daily rainfall time series from 15 countries in the Central Eastern Europe through different lagged and non-lagged indices of associations that quantify both the overall dependence and extreme dependence of pairwise observations. We also show that these measures are linked to each other and can be written in a unique and coherent notation within the copula framework. Moreover, the lagged version of these measures allows exploring some important spatio-temporal properties of the rainfall fields. The exploratory analysis is complemented by the preliminary results of a spatio-temporal rainfall simulation performed via a compound model based upon the Generalized Additive Models for Location, Scale and Shape (GAMLSS) and meta-elliptical multivariate distributions.

Geographic information systems form a core part of Earth Science education and teaching, allowing the ever-growing repositories of digital geo-data to be integrated and visualised in a unified fashion. These systems cope with the wide variety of spatial data types, each with their own properties and metadata, allowing for a better understanding of how Earth processes operate. A unique requirement for the Earth Sciences is to take into account plate motion and crustal deformation processes acting through time, thus altering the various spatial relationships. The open-source GPlates software (www.gplates.org) infrastructure has become a standard tool for this type of analysis, providing the ability to reconstruct various datasets through time interactively by attaching arbitrary data to tectonic plates. Combining vast datasets in this manner is increasing the analysis complexity, with traditional visualisation-based approaches becoming ineffective in extracting necessary information and discovering new insights. In addressing this, GPlates has been extended with two key technologies, manifesting itself as a powerful interactive knowledge-discovery platform. The first technology is a "data coregistration" tool, in which desired relationships between various datasets are recursively defined, thus providing the key link between a qualitative visualisation environment and a quantitative multivariate statistical analysis framework. The second technology is a data-mining environment (Orange, http://orange.biolab.si), better suited to coping with complexities due to large datasets, high dimensionality, spatial and temporal dynamics, different data types etc. The data-mining tool has a diverse library of components allowing for interactive filtering, combining, transforming and pattern analysis of incoming data. Attached to the data-mining tool is a visual-programming environment in which underlying software complexities are abstracted from the user, allowing for the rapid prototyping of analysis work-flows without requiring programming expertise. A plug-in framework allows for the construction of new spatio-temporal data processing components, which is seeing the functionality and flexibility of this environment increasing rapidly, aided by an open-source model. The resultant ensemble of technologies lends itself to becoming a frontier teaching and research tool, providing the necessary abstraction of complexity required to better understand how the various complex Earth processes acted through time resulting in the familiar spatial configuration we observe today.

1 Convolutive BlindSource Separation based on Multiple Decorrelation. Lucas Parra, Clay Spence published in re- cent years on the problem of blindsource separation. For one, the problem seems nd an FIR backward model, which generates well separated model sources. Furthermore, for more than

for the blind signal separation task 1]{ 12]. Such methodsuse higher-order statisticalinformationaboutthe sourceMULTICHANNEL BLIND SEPARATION AND DECONVOLUTION OF SOURCES WITH ARBITRARY DISTRIBUTIONS Scott C non-Gaussian sources. Our technique monitors the statistics of each of the outputs of the sepa- rator

Natural grasses are found in semiarid rangelands with disperse tree cover of part of the Iberian Peninsula and constitute a resource with high ecologic and economic value worth, being an important source of food for livestock, playing a significant role in the hydrologic cycle, controlling the soil thermal regime, and are a key factor in reducing soil erosion and degradation. However, increasing pressure on the resources, changes in land use as well as possible climate variations threaten the sustainability of natural grasses. Despite of their importance, the spatio-temporal variations of pasture production over whole watersheds are poorly known. In this sense, previous studies by other authors have indicated its dependence on a balance of positive and negative effects brought about by the main limiting factors: water, light, nutrients and space. Nevertheless, the specific weight of each factor is not clear because they are highly variable due to climate characteristics and the structure of these agroforestry systems. We have used a physical spatially-distributed ecohydrologic model to investigate the specific weight of factors that contribute to pasture production in a semiarid watershed of 99.5 ha in western Spain. This model couples a two layer (canopy and understory) vertical local closure energy balance scheme, a hydrologic model and a carbon uptake and vegetation growth component, and it was run using a synthetic daily climate dataset generated by a stochastic weather generator, which reproduced the range of climatic variations observed under mediterranean current climate. The modelling results reproduced satisfactorily the seasonality effects of climate as precipitation and temperatures, as well as annual and inter-annual variations of pasture production. Spatial variations of pasture production were largely controlled by topographic and tree effects, showing medium-low values depending of considered areas. These low values require introduction of feed to livestock. Valley bottoms, areas with low slopes, and spaces with low tree density are characterized by higher pasture production. Temporal variations of pasture production largely depended on the availability of soil moisture, which in turn depended on the temporal distribution of rainfall. This ecohydrologic model constitutes a valuable tool to investigate water and energy fluxes, as well as vegetation dynamics in semiarid rangelands, as was proved by a quantitative assessment of the quality of the simulations. The range of applications and possibilities contained in the model opens a wide field for future research.

Understanding the intensity and spatial patterns of animal transfers is of prime importance as geographical moves play an important part in the spread and potential control of contagious animal diseases of veterinary importance. For the purpose of visualizing all registered between-herd animal movements in Sweden between 1 July 2005 and 31 December 2008 by map animation, a grid network technique based on the Bresenham line algorithm was developed. Potential spatio-temporal clustering of animals registered as sold or purchased based on location and month of trade was also detected and tested using a spatial scan statistic. Calculations were based on data from 31,375 holdings and 3,487,426 head of cattle. In total, 988,167 between-herd movements of individual bovines were displayed in a sequence of maps covering three and a half years by 2-week intervals. The maps showed that several cattle movements, both short- and long-distance, take place in Sweden each week of the year. However, most animals (75%) were only registered at one single holding during the study period and 23% were sold to a different holding once. Spatial scan statistics based on data from the year 2008 indicated uneven distributions of purchased or sold animals in space and time. During each autumn, there was an increase in cattle movements and October and November showed significantly more cases of sold or purchased animals (relative risk ~1.7, p = 0.001). Based on the results, we conclude that cattle trade is constantly active at a considerable level. This, in combination with possibly insufficient biosecurity routines applied on many farms, constitutes a risk that contagious diseases are spread in the population. The grid network maps were generated through the use of open-source tools and software in order to decrease software costs and facilitate sharing of programme code. In addition, the technique was based on scripts that allow for the inclusion of iterative processes and that comprise all main parts of map creation. Thereby, a large number of maps can be generated and the demands for high reproducibility are met. PMID:21080326

EXPLOITING SOURCE NON STATIONARY AND COLORATION IN BLINDSOURCE SEPARATION Dinh Tuan Pham, France e-mail: Dinh-Tuan.Pham@imag.fr ABSTRACT A new method for blindsources separation of instan). It is then well known that blind separation can be achieved only if all sources (except possible one) are non

This paper considers the use of combined spatial and temporal filtering to improve the quality of a structural health monitoring (SHM) damage estimate. Many SHM systems produce two-dimensional (2-D) array data that contains structure damage estimate, which is distorted by noise from sensor sources, changing environmental conditions, and other inspection factors. We describe a filtering architecture for processing a sequence

Background With the successful implementation of integrated measures for schistosomiasis japonica control, Jiangsu province has reached low-endemicity status. However, infected Oncomelania hupensis snails could still be found in certain locations along the Yangtze river until 2009, and there is concern that they might spread again, resulting in the possible re-emergence of infections among people and domestic animals alike. In order to establish a robust surveillance system that is able to detect the spread of infected snails at an early stage, sensitive and reliable methods to identify risk factors for the establishment of infected snails need to be developed. Methods A total of 107 villages reporting the persistent presence of infected snails were selected. Relevant data on the distribution of infected snails, and human and livestock infection status information for the years 2003 to 2008 were collected. Spatio-temporal pattern analysis including spatial autocorrelation, directional distribution and spatial error models were carried out to explore spatial correlations between infected snails and selected explanatory factors. Results The area where infected snails were found, as well as their density, decreased significantly between 2003 and 2008. Changes in human and livestock prevalences were less pronounced. Three statistically significant spatial autocorrelations for infected snails were identified. (i) The Moran’s I of infected snails increased from 2004 to 2007, with the snail density increasing and the area with infected snails decreasing. (ii) The standard deviations of ellipses around infected snails were decreasing and the central points of the ellipses moved from West to East. (iii) The spatial error models indicated no significant correlation between the density of infected snails and selected risk factors. Conclusions We conclude that the contribution of local infection sources including humans and livestock to the distribution of infected snails might be relatively small and that snail control may limit infected snails to increasingly small areas ecologically most suitable for transmission. We provide a method to identify these areas and risk factors for persistent infected snail presence through spatio-temporal analysis, and a suggested framework, which could assist in designing evidence based control strategies for schistosomiasis japonica elimination. PMID:23648203

This paper introduces and describes the hourly, high-resolution soil moisture dataset continuously recorded by the McMaster Mesonet located in the Hamilton-Halton Watershed in Southern Ontario, Canada. The McMaster Mesonet consists of a network of time domain reflectometer (TDR) probes collecting hourly soil moisture data at six depths between 10 cm and 100 cm at nine locations per site, spread across four sites in the 1250 km2 watershed. The sites for the soil moisture arrays are designed to further improve understanding of soil moisture dynamics in a seasonal climate and to capture soil moisture transitions in areas that have different topography, soil and land cover. The McMaster Mesonet soil moisture constitutes a unique database in Canada because of its high spatio-temporal resolution. In order to provide some insight into the dominant processes at the McMaster Mesonet sites, a spatio-temporal and temporal stability analysis were conducted to identify spatio-temporal patterns in the data and to suggest some physical interpretation of soil moisture variability. It was found that the seasonal climate of the Great Lakes Basin causes a transition in soil moisture patterns at seasonal timescales. During winter and early spring months, and at the meadow sites, soil moisture distribution is governed by topographic redistribution, whereas following efflorescence in the spring and summer, soil moisture spatial distribution at the forested site was also controlled by vegetation canopy. Analysis of short-term temporal stability revealed that the relative difference between sites was maintained unless there was significant rainfall (> 20 mm) or wet conditions a priori. Following a disturbance in the spatial soil moisture distribution due to wetting, the relative soil moisture pattern re-emerged in 18 to 24 h. Access to the McMaster Mesonet data can be provided by visiting www.hydrology.mcmaster.ca/mesonet.

In this paper, a detailed investigation of the spatio-temporal dynamics of a pulsed microwave plasma is presented. The plasma is ignited inside a dielectric tube in a repetitively pulsed regime at pressures ranging from 1 up to 100 mbar with pulse repetition frequencies from 200 Hz up to 500 kHz. Various diagnostic techniques are employed to obtain the main plasma parameters both spatially and with high temporal resolution. Thomson scattering is used to obtain the electron density and mean electron energy at fixed positions in the dielectric tube. The temporal evolution of the two resonant and two metastable argon 4s states are measured by laser diode absorption spectroscopy. Nanosecond time-resolved imaging of the discharge allows us to follow the spatio-temporal evolution of the discharge with high temporal and spatial resolution. Finally, the temporal evolution of argon 4p and higher states is measured by optical emission spectroscopy. The combination of these various diagnostics techniques gives deeper insight on the plasma dynamics during pulsed microwave plasma operation from low to high pressure regimes. The effects of the pulse repetition frequency on the plasma ignition dynamics are discussed and the plasma-off time is found to be the relevant parameter for the observed ignition modes. Depending on the delay between two plasma pulses, the dynamics of the ionization front are found to be changing dramatically. This is also reflected in the dynamics of the electron density and temperature and argon line emission from the plasma. On the other hand, the (quasi) steady state properties of the plasma are found to depend only weakly on the pulse repetition frequency and the afterglow kinetics present an uniform spatio-temporal behavior. However, compared to continuous operation, the time-averaged metastable and resonant state 4s densities are found to be significantly larger around a few kHz pulsing frequency.

The spatio-temporal pattern of auditory nerve (AN) activity, representing the relative timing of spikes across the tonotopic axis, contains cues to perceptual features of sounds such as pitch, loudness, timbre, and spatial location. These spatio-temporal cues may be extracted by neurons in the cochlear nucleus (CN) that are sensitive to relative timing of inputs from AN fibers innervating different cochlear regions. One possible mechanism for this extraction is “cross-frequency” coincidence detection (CD), in which a central neuron converts the degree of coincidence across the tonotopic axis into a rate code by preferentially firing when its AN inputs discharge in synchrony. We used Huffman stimuli (Carney LH. J Neurophysiol 64: 437–456, 1990), which have a flat power spectrum but differ in their phase spectra, to systematically manipulate relative timing of spikes across tonotopically neighboring AN fibers without changing overall firing rates. We compared responses of CN units to Huffman stimuli with responses of model CD cells operating on spatio-temporal patterns of AN activity derived from measured responses of AN fibers with the principle of cochlear scaling invariance. We used the maximum likelihood method to determine the CD model cell parameters most likely to produce the measured CN unit responses, and thereby could distinguish units behaving like cross-frequency CD cells from those consistent with same-frequency CD (in which all inputs would originate from the same tonotopic location). We find that certain CN unit types, especially those associated with globular bushy cells, have responses consistent with cross-frequency CD cells. A possible functional role of a cross-frequency CD mechanism in these CN units is to increase the dynamic range of binaural neurons that process cues for sound localization. PMID:22972956

Spatio-temporal patterns of dengue risk in Malaysia were studied both at the address and the sub-district level in the province of Selangor and the Federal Territory of Kuala Lumpur. We geocoded laboratory-confirmed dengue cases from the years 2008 to 2010 at the address level and further aggregated the cases in proportion to the population at risk at the sub-district level. Kulldorff's spatial scan statistic was applied for the investigation that identified changing spatial patterns of dengue cases at both levels. At the address level, spatio-temporal clusters of dengue cases were concentrated at the central and south-eastern part of the study area in the early part of the years studied. Analyses at the sub-district level revealed a consistent spatial clustering of a high number of cases proportional to the population at risk. Linking both levels assisted in the identification of differences and confirmed the presence of areas at high risk for dengue infection. Our results suggest that the observed dengue cases had both a spatial and a temporal epidemiological component, which needs to be acknowledged and addressed to develop efficient control measures, including spatially explicit vector control. Our findings highlight the importance of detailed geographical analysis of disease cases in heterogeneous environments with a focus on clustered populations at different spatial and temporal scales. We conclude that bringing together information on the spatio-temporal distribution of dengue cases with a deeper insight of linkages between dengue risk, climate factors and land use constitutes an important step towards the development of an effective risk management strategy. PMID:25545931

Coupled Map Lattices (CML) can be interpreted as spatio-temporal fitness landscapes which may pose a dynamic optimization problem. In this paper, we analyze such dynamic fitness landscapes in terms of the landscape measures modality, ruggedness, information content and epistasis. These measures account for different aspects of problem hardness. We use an evolutionary algorithm to solve the dynamic optimization problem and study the relationship between performance criteria of the algorithm and the landscape measures. In this way we relate problem hardness to expectable performance.

We study the relationship between synchronization and the rate with which information is exchanged between nodes in a spatio-temporal network that describes the dynamics of classical particles under a substrate Remoissenet-Peyrard potential. We also show how phase and complete synchronization can be detected in this network. The difficulty in detecting phase synchronization in such a network appears due to the highly non-coherent character of the particle dynamics which unables a proper definition of the phase dynamics. The difficulty in detecting complete synchronization appears due to the spatio character of the potential which results in an asymptotic state highly dependent on the initial state.

We study the phase diagram of the sine circle map lattice with random initial conditions and identify the various types of dynamical behaviour which appear here. We focus on the regions which show spatio-temporal intermittency and characterise the accompanying scaling behaviour. Directed percolation exponents are seen at some points in the parameter space in the neighbourhood of bifurcation boundaries. We discuss this behaviour as well as other types of behaviour seen in the parameter space in the context of the phase diagram.

Chemical reactions far from equilibrium on solid surfaces may exhibit typical phenomena of nonlinear dynamics, as exemplified by the catalytic oxidation of carbon monoxide on a platinum(110) single-crystal surface. Depending on the external parameters (temperature and partial pressures of the reactants), the temporal variation of the reaction rate may become oscillatory or even chaotic. In a parallel way, the concentration distributions of the adsorbed species on the surface form spatio-temporal patterns including propagating and standing waves, rotating spirals, as well as irregular and rapidly changing structures denoted "chemical turbulence." PMID:17829239

1. Drought is a natural disturbance that can cause widespread mortality of aquatic organisms in wetlands. We hypothesized that seasonal drying of marsh surfaces (i.e. hydrological disturbance) shapes spatio-temporal patterns of fish populations. 2. We tested whether population dynamics of fishes were synchronized by hydrological disturbance (Moran effect) or distance separating study sites (dispersal). Spatio-temporal patterns were examined in local populations of five abundant species at 17 sites (sampled five times per year from 1996 to 2001) in a large oligotrophic wetland. 3. Fish densities differed significantly across spatio-temporal scales for all species. For all species except eastern mosquitofish (Gambusia holbrooki), a significant portion of spatio-temporal variation in density was attributed to drying events (used as a covariate). 4. We observed three patterns of response to hydrological disturbance. Densities of bluefin killifish (Lucania goodei), least killifish (Heterandria formosa), and golden top-minnow (Fundulus chrysotus) were usually lowest after a dry down and recovered slowly. Eastern mosquitofish showed no distinct response to marsh drying (i.e. they recovered quickly). Flagfish (Jordanella floridae) density was often highest after a dry down and then declined. Population growth after a dry down was often asymptotic for bluefin killifish and golden topminnow, with greatest asymptotic density and longest time to recovery at sites that dried infrequently. 5. Fish population dynamics were synchronized by hydrological disturbance (independent of distance) and distance separating study sites (independent of hydrological disturbance). Our ability to separate the relative importance of the Moran effect from dispersal was strengthened by a weak association between hydrological synchrony and distance among study sites. Dispersal was the primary mechanism for synchronous population dynamics of flagfish, whereas hydrological disturbance was the primary mechanism for synchronous population dynamics of the other species examined. 6. Species varied in the relative role of the Moran effect and dispersal in homogenizing their population dynamics, probably as a function of life history and ability to exploit dry-season refugia. ?? 2005 British Ecological Society.

Spatio-temporal statistical models are increasingly being used across a wide variety of scientific disciplines to describe and predict spatially-explicit processes that evolve over time. Correspondingly, in recent years there has been a significant amount of research on new statistical methodology for such models. Although descriptive models that approach the problem from the second-order (covariance) perspective are important, and innovative work is being done in this regard, many real-world processes are dynamic, and it can be more efficient in some cases to characterize the associated spatio-temporal dependence by the use of dynamical models. The chief challenge with the specification of such dynamical models has been related to the curse of dimensionality. Even in fairly simple linear, first-order Markovian, Gaussian error settings, statistical models are often over parameterized. Hierarchical models have proven invaluable in their ability to deal to some extent with this issue by allowing dependency among groups of parameters. In addition, this framework has allowed for the specification of science based parameterizations (and associated prior distributions) in which classes of deterministic dynamical models (e. g., partial differential equations (PDEs), integro-difference equations (IDEs), matrix models, and agent-based models) are used to guide specific parameterizations. Most of the focus for the application of such models in statistics has been in the linear case. The problems mentioned above with linear dynamic models are compounded in the case of nonlinear models. In this sense, the need for coherent and sensible model parameterizations is not only helpful, it is essential. Here, we present an overview of a framework for incorporating scientific information to motivate dynamical spatio-temporal models. First, we illustrate the methodology with the linear case. We then develop a general nonlinear spatio-temporal framework that we call general quadratic nonlinearity and demonstrate that it accommodates many different classes of scientific-based parameterizations as special cases. The model is presented in a hierarchical Bayesian framework and is illustrated with examples from ecology and oceanography. ?? 2010 Sociedad de Estad??stica e Investigaci??n Operativa.

Ischemic heart disease (IHD) is a leading cause of death worldwide. Urban public health and medical management in Shenzhen, an international city in the developing country of China, is challenged by an increasing burden of IHD. This study analyzed the spatio-temporal variation of IHD hospital admissions from 2003 to 2012 utilizing spatial statistics, spatial analysis, and space-time scan statistics. The spatial statistics and spatial analysis measured the incidence rate (hospital admissions per 1,000 residents) and the standardized rate (the observed cases standardized by the expected cases) of IHD at the district level to determine the spatio-temporal distribution and identify patterns of change. The space-time scan statistics was used to identify spatio-temporal clusters of IHD hospital admissions at the district level. The other objective of this study was to forecast the IHD hospital admissions over the next three years (2013-2015) to predict the IHD incidence rates and the varying burdens of IHD-related medical services among the districts in Shenzhen. The results show that the highest hospital admissions, incidence rates, and standardized rates of IHD are in Futian. From 2003 to 2012, the IHD hospital admissions exhibited similar mean centers and directional distributions, with a slight increase in admissions toward the north in accordance with the movement of the total population. The incidence rates of IHD exhibited a gradual increase from 2003 to 2012 for all districts in Shenzhen, which may be the result of the rapid development of the economy and the increasing traffic pollution. In addition, some neighboring areas exhibited similar temporal change patterns, which were also detected by the spatio-temporal cluster analysis. Futian and Dapeng would have the highest and the lowest hospital admissions, respectively, although these districts have the highest incidence rates among all of the districts from 2013 to 2015 based on the prediction using the GM (1,1). In addition, the combined analysis of the prediction of IHD hospital admissions and the general hospital distributions shows that Pingshan and Longgang might experience the most serious burden of IHD hospital services in the near future, although Futian would still have the greatest number and the highest incidence rate of hospital admissions for IHD. PMID:24806191

Neuroimaging data demonstrate that carpal tunnel syndrome, a peripheral neuropathy, is accompanied by maladaptive central neuroplasticity. To further investigate this phenomenon, we collected magnetoencephalography data from 12 patients with carpal tunnel syndrome and 12 healthy control subjects undergoing somatosensory stimulation of the median nerve-innervated Digits 2 and 3, as well as Digit 5, which is innervated by the ulnar nerve. Nerve conduction velocity and psychophysical data were acquired to determine whether standard clinical measures correlated with brain response. In subjects with carpal tunnel syndrome, but not healthy controls, sensory nerve conduction velocity for Digits 2 and 3 was slower than Digit 5. However, somatosensory M20 latencies for Digits 2 and 3 were significantly longer than those of Digit 5. The extent of the M20 delay for median nerve-innervated Digit 2 was positively correlated with decreasing nerve conduction velocity and increasing pain severity. Thus, slower peripheral nerve conduction in carpal tunnel syndrome corresponds to greater delays in the first somatosensory cortical response. Furthermore, spectral analysis demonstrated weaker post-stimulus beta event-related desynchronization and earlier and shorter event-related synchronization in subjects with carpal tunnel syndrome. The extent of the decreased event-related desynchronization for median nerve-innervated digits was positively correlated with paraesthesia severity. We propose that ongoing paraesthesias in median nerve-innervated digits render their corresponding sensorimotor cortical areas ‘busy’, thus reducing their capacity to process external stimulation. Finally, subjects with carpal tunnel syndrome demonstrated a smaller cortical source separation for Digits 2 and 3 compared with healthy controls. This supports our hypothesis that ongoing paraesthesias promote blurring of median nerve-innervated digit representations through Hebbian plasticity mechanisms. In summary, this study reveals significant correlation between the clinical severity of carpal tunnel syndrome and the latency of the early M20, as well as the strength of long latency beta oscillations. These temporal magnetoencephalography measures are novel markers of neuroplasticity in carpal tunnel syndrome and could be used to study central changes that may occur following clinical intervention. PMID:23043143

We use a simple multiplicative spatio-temporal model to describe variability in a sequence of water quality monitoring data from headwater streams in the Conwy catchment, North Wales. The spatial component of the model treats concentrations as due to simple mixing of a small number of distinct source types, each associated with particular upstream catchment characteristics. The temporal component allows concentration variability due to seasonal or hydrological change. We apply the model using three candidate catchment characteristic classifications to generate mixing concentrations, and a seasonal component to describe temporal variability, and test a range of sub-models. We identify a cross-classification of soil and land cover as providing the best spatial indicator of water quality of the classifications considered. The spatial model based on a selected grouped cross-classification was shown to account for between 35% and 90% of the spatial variability and the seasonal model accounted for between 45% and 100% of the temporal variability in the data. Analysis of residuals showed an inverse relationship between DOC and sulphate and between hydrogen ion concentration and calcium and magnesium. We also found residual correlations between sites which are strongly related to landscape class. These are attributed to landscape class by time interactions which are not accounted for in the simple multiplicative model. PMID:24509947

Blindsource separation (BSS) is a challenging problem in real-world environments where sources are time delayed and convolved. The problem becomes more difficult in very reverberant conditions, with an increasing number of sources, and geometric configurations of the sources such that finding directionality is not sufficient for source separation. In this paper, we propose a new algorithm that exploits higher

Miniaturization is a powerful trend for smart chemical instrumentation in a diversity of applications. It is know that miniaturization in IMS leads to a degradation of the system characteristics. For the present work, we are interested in signal processing solutions to mitigate limitations introduced by limited drift tube length that basically involve a loss of chemical selectivity. While blindsource separation techniques (BSS) are popular in other domains, their application for smart chemical instrumentation is limited. However, in some conditions, basically linearity, BSS may fully recover the concentration time evolution and the pure spectra with few underlying hypothesis. This is extremely helpful in conditions where non-expected chemical interferents may appear, or unwanted perturbations may pollute the spectra. SIMPLISMA has been advocated by Harrington et al. in several papers. However, more modern methods of BSS for bilinear decomposition with the restriction of positiveness have appeared in the last decade. In order to explore and compare the performances of those methods a series of experiments were performed.

A novel blind separation approach using higher-order time-frequency distributions is presented. The concept of higher-order time-frequency distribution matrix is also introduced. It is devised to primarily separate sources with temporal nonstationary signal characteristics. So far, this problem has been solved using statistical information available on the source signals. In contrast to well known blindsource separation approaches using second-order statistics

Flume experiments were conducted to investigate the spatio–temporal structure of subaqueous particulate gravity flows with an initial concentration of 14% by volume. Time series of downstream flow velocity and its calculated degree of turbulence, median grain size and sediment concentration at different positions along the path of nominally identical flows are analysed and combined to constrain the spatio–temporal evolution of

Carnivorous plants have evolved modified leaves into the traps that assist in nutrient uptake from captured prey. It is known that the traps of carnivorous plants usually have lower photosynthetic rates than assimilation leaves as a result of adaptation to carnivory. However, a few recent studies have indicated that photosynthesis and respiration undergo spatio-temporal changes during prey capture and retention, especially in the genera with active trapping mechanisms. This study describes the spatio-temporal changes of effective quantum yield of photochemical energy conversion in photosystem II (?PSII) in response to ant-derived formic acid during its capture and digestion. PMID:20523127

A key feature of extreme ultraviolet (XUV) radiation from free-electron lasers (FELs) is its spatial and temporal coherence. We measured the spatio-temporal coherence properties of monochromatized FEL pulses at 13.5?nm using a Michelson interferometer. A temporal coherence time of (59±8) fs has been determined, which is in good agreement with the spectral bandwidth given by the monochromator. Moreover, the spatial coherence in vertical direction amounts to about 15% of the beam diameter and about 12% in horizontal direction. The feasibility of measuring spatio-temporal coherence properties of XUV FEL radiation using interferometric techniques advances machine operation and experimental studies significantly.

Turning is a requirement for most locomotor tasks; however, knowledge of the biomechanical requirements of successful turning is limited. Therefore, the aims of this study were to investigate the spatio-temporal and lower-limb kinematics of 90° turning. Seventeen typically developing children, fitted with full body and multi-segment foot marker sets, having performed both step (outside leg) and spin (inside leg) turning strategies at self-selected velocity, were included in the study. Three turning phases were identified: approach, turn, and depart. Stride velocity and stride length were reduced for both turning strategies for all turning phases (p<0.03 and p<0.01, respectively), while stance time and stride width were increased during only select phases (p<0.05 and p<0.01, respectively) for both turn conditions compared to straight gait. Many spatio-temporal differences between turn conditions and phases were also found (p<0.03). Lower-limb kinematics revealed numerous significant differences mainly in the coronal and transverse planes for the hip, knee, ankle, midfoot, and hallux between conditions (p<0.05). The findings summarized in this study help explain how typically developing children successfully execute turns and provide greater insight into the biomechanics of turning. This knowledge may be applied to a clinical setting to help improve the management of gait disorders in pathological populations, such as children with cerebral palsy. PMID:23684101

In this paper, we present a mathematical description for excitable biological membranes, in particular neuronal membranes. We aim to model the (spatio-) temporal dynamics, e.g., the travelling of an action potential along the axon, subject to noise, such as ion channel noise. Using the framework of Piecewise Deterministic Processes (PDPs) we provide an exact mathematical description-in contrast to pseudo-exact algorithms considered in the literature-of the stochastic process one obtains coupling a continuous time Markov chain model with a deterministic dynamic model of a macroscopic variable, that is coupling Markovian channel dynamics to the time-evolution of the transmembrane potential. We extend the existing framework of PDPs in finite dimensional state space to include infinite-dimensional evolution equations and thus obtain a stochastic hybrid model suitable for modelling spatio-temporal dynamics. We derive analytic results for the infinite-dimensional process, such as existence, the strong Markov property and its extended generator. Further, we exemplify modelling of spatially extended excitable membranes with PDPs by a stochastic hybrid version of the Hodgkin-Huxley model of the squid giant axon. Finally, we discuss the advantages of the PDP formulation in view of analytical and numerical investigations as well as the application of PDPs to structurally more complex models of excitable membranes. PMID:21243359

The collective dynamics of neural ensembles create complex spike patterns with many spatial and temporal scales. Understanding the statistical structure of these patterns can help resolve fundamental questions about neural computation and neural dynamics. Spatio-temporal conditional inference (STCI) is introduced here as a semiparametric statistical framework for investigating the nature of precise spiking patterns from collections of neurons that is robust to arbitrarily complex and nonstationary coarse spiking dynamics. The main idea is to focus statistical modeling and inference, not on the full distribution of the data, but rather on families of conditional distributions of precise spiking given different types of coarse spiking. The framework is then used to develop families of hypothesis tests for probing the spatio-temporal precision of spiking patterns. Relationships among different conditional distributions are used to improve multiple hypothesis testing adjustments and to design novel Monte Carlo spike resampling algorithms. Of special note are algorithms that can locally jitter spike times while still preserving the instantaneous peri-stimulus time histogram (PSTH) or the instantaneous total spike count from a group of recorded neurons. The framework can also be used to test whether first-order maximum entropy models with possibly random and time-varying parameters can account for observed patterns of spiking. STCI provides a detailed example of the generic principle of conditional inference, which may be applicable in other areas of neurostatistical analysis. PMID:25380339

Spatio-temporal dynamics in land surface phenology parameters observed over croplands can inform on crop-climate interactions and, elucidate local to regional scale vulnerabilities either due to climate change or prevailing sub-optimal agricultural practices. Here, we observe spatio-temporal trends in land surface phenology parameters (cropping intensity, length of growing season and productivity) for kharif and rabi cropping seasons from satellite data across the Indo-Gangetic Plains from 1982 to 2006. The productivity of the Indo-Gangetic Plains croplands is of regional importance and is a vital component of Indian national food security efforts. Aside from local and intra-state heterogeneity in observed trends there was a clear west-to-east gradient in cropping intensity. Key observed trends include increasing cropping intensity in the eastern IGP, increasing number of growing days per year in Bihar, Uttar Pradesh and Haryana and increasing productivity in both cropping seasons across the IGP. This information is a crucial input to integrated assessments of the croplands to ensure management of the agricultural system shifts towards a trajectory of climate-resilience and environmental sustainability. To create spatially explicit time-series, at a spatial resolution of 8 km across the IGP of the following LSP parameters: (i) cropping intensity, (ii) LGS and (iii) agro-ecosystem productivity. To quantify normal conditions, inter-annual variation and long-term trends in these LSP parameters at an 8 km spatial resolution across the IGP croplands.

During spontaneous cell polarization of Dictyostelium discoideum cells, phosphatidylinositol (3,4,5)-triphoshpate (PIP3) and PTEN (phosphatase tensin homolog) have been identified as key signaling molecules which govern the process of polarization in a self-organized manner. Recent experiments have quantified the spatio-temporal dynamics of these signaling components. Surprisingly, it was found that membrane-bound PTEN can be either in a high or low state, that PIP3 waves were initiated in areas lacking PTEN through an excitable mechanism, and that PIP3 was degraded even though the PTEN concentration remained low. Here we develop a reaction-diffusion model that aims to explain these experimental findings. Our model contains bistable dynamics for PTEN, excitable dynamics for PIP3, and postulates the existence of two species of PTEN with different dephosphorylation rates. We show that our model is able to produce results that are in good qualitative agreement with the experiments, suggesting that our reaction-diffusion model underlies the self-organized spatio-temporal patterns observed in experiments. PMID:25024302

Stem cells integrate signals from the microenvironment to generate lineage-specific gene expression programs upon differentiation. Undifferentiated cell nuclei are easily deformable, with an active transcriptome, whereas differentiated cells have stiffer nuclei and condensed chromatin. Chromatin organization in the stem cell state is known to be highly dynamic but quantitative characterizations of its plasticity are lacking. Using fluorescence imaging, we study the spatio-temporal dynamics of nuclear architecture and chromatin compaction in mouse embryonic stem (ES) cells and differentiated states. Individual ES cells exhibit a relatively narrow variation in chromatin compaction, whereas primary mouse embryonic fibroblasts (PMEF) show broad distributions. However, spatial correlations in chromatin compaction exhibit an emergent length scale in PMEFs, although they are unstructured and longer ranged in ES cells. We provide evidence for correlated fluctuations with large amplitude and long intrinsic timescales, including an oscillatory component, in both chromatin compaction and nuclear area in ES cells. Such fluctuations are largely frozen in PMEF. The role of actin and Lamin A/C in modulating these fluctuations is described. A simple theoretical formulation reproduces the observed dynamics. Our results suggest that, in addition to nuclear plasticity, correlated spatio-temporal structural fluctuations of chromatin in undifferentiated cells characterize the stem cell state. PMID:23442906

Pattern formation often occurs in spatially extended physical, biological, and chemical systems due to an instability of the homogeneous steady state. The type of the instability usually prescribes the resulting spatio-temporal patterns and their characteristic length scales. However, patterns resulting from the simultaneous occurrence of instabilities cannot be expected to be simple superposition of the patterns associated with the considered instabilities. To address this issue, we design two simple models composed by two asymmetrically coupled equations of non-conserved (Swift-Hohenberg equations) or conserved (Cahn-Hilliard equations) order parameters with different characteristic wave lengths. The patterns arising in these systems range from coexisting static patterns of different wavelengths to traveling waves. A linear stability analysis allows to derive a two parameter phase diagram for the studied models, in particular, revealing for the Swift-Hohenberg equations, a co-dimension two bifurcation point of Turing and wave instability and a region of coexistence of stationary and traveling patterns. The nonlinear dynamics of the coupled evolution equations is investigated by performing accurate numerical simulations. These reveal more complex patterns, ranging from traveling waves with embedded Turing patterns domains to spatio-temporal chaos, and a wide hysteretic region, where waves or Turing patterns coexist. For the coupled Cahn-Hilliard equations the presence of a weak coupling is sufficient to arrest the coarsening process and to lead to the emergence of purely periodic patterns. The final states are characterized by domains with a characteristic length, which diverges logarithmically with the coupling amplitude.

In a globalised world where risks spread through contagion, the decision of an entity to invest in securing its premises from stochastic risks no longer depends solely on its own actions but also on the actions of other interacting entities in the system. This phenomenon is commonly seen in many domains including airline, logistics and computer security and is referred to as Interdependent Security (IDS). An IDS game models this decision problem from a game-theoretic perspective and deals with the behavioural dynamics of risk-reduction investments in such settings. This paper enhances this model and investigates the spatio-temporal aspects of the IDS games. The spatio-temporal dynamics are studied using simple replicator dynamics on a variety of network structures and for various security cost tradeoffs that lead to different Nash equilibria in an IDS game. The simulation results show that the neighbourhood configuration has a greater effect on the IDS game dynamics than network structure. An in-depth empirical analysis of game dynamics is carried out on regular graphs, which leads to the articulation of necessary and sufficient conditions for dominance in IDS games under spatial constraints.

Earthquake clustering tends to be an increasingly important part of general earthquake research especially in terms of seismic hazard assessment and earthquake forecasting and prediction approaches. The distinct identification and definition of foreshocks, aftershocks, mainshocks and secondary mainshocks is taken into account using a point based spatio-temporal clustering algorithm originating from the field of classic machine learning. This can be further applied for declustering purposes to separate background seismicity from triggered seismicity. The results are interpreted and processed to assemble 3D-(x,y,t) earthquake clustering maps which are based on smoothed seismicity records in space and time. In addition, multi-dimensional Gaussian functions are used to capture clustering parameters for spatial distribution and dominant orientations. Clusters are further processed using methodologies originating from geostatistics, which have been mostly applied and developed in mining projects during the last decades. A 2.5D variogram analysis is applied to identify spatio-temporal homogeneity in terms of earthquake density and energy output. The results are mitigated using Kriging to provide an accurate mapping solution for clustering features. As a case study, seismic data of New Zealand and the United States is used, covering events since the 1950s, from which an earthquake cluster catalogue is assembled for most of the major events, including a detailed analysis of the Landers and Christchurch sequences.

the sample block circulant covariance matrix by enforcing two essential properties: rank and FIR structure-temporal channels over the past decade, based on the singularity of the received signal power spectral density matrix [3]. This singularity can be exploited to separate the white noise contribution. The main problem

This paper presents a method for enhancing a dominant target source that is close to sensors, and suppressing other interferences. The enhancement is performed blindly, i.e. without knowing the number of total sources or information about each source, such as position and active time. We consider a general case where the number of sources is larger than the number of

We systemically identify repeating earthquakes and investigate spatio-temporal variations of fault zone properties associated with the 2004 Mw6.0 Parkfield earthquake along the Parkfield section of the San Andreas fault, and the 1984 Mw6.2 Morgan Hill earthquake along the central Calaveras fault. The procedure for identifying repeating earthquakes is based on overlapping of the source regions and the waveform similarity, and is briefly described as follows. First, we estimate the source radius of each event based on a circular crack model and a normal stress drop of 3 MPa. Next, we compute inter-hypocentral distance for events listed in the relocated catalog of Thurber et al. (2006) around Parkfield, and Schaff et al. (2002) along the Calaveras fault. Then, we group all events into 'initial' clusters by requiring the separation distance between each event pair to be less than the source radius of larger event, and their magnitude difference to be less than 1. Next, we calculate the correlation coefficients between every event pair within each 'initial' cluster using a 3-s time window around the direct P waves for all available stations. The median value of the correlation coefficients is used as a measure of similarity between each event pair. We drop an event if the median similarity to the rest events in that cluster is less than 0.9. After identifying repeating clusters in both regions, our next step is to apply a sliding window waveform cross-correlation technique (Niu et al., 2003; Peng and Ben-Zion, 2006) to calculate the delay time and decorrelation index for each repeating cluster. By measuring temporal changes in waveforms of repeating clusters at different locations and depth, we hope to obtain a better constraint on spatio-temporal variations of fault zone properties and near-surface layers associated with the occurrence of major earthquakes.

Blindsource separation is discussed with more sources than mixtures in this paper. The blind separation technique includes two steps. The 1rst step is to estimate a mixing matrix, and the second is to estimate sources. Ifthe sources are sparse, the mixing matrix can be estimated by using the generalized exponential mixture model. The generalized exponential mixture model is a

BACKGROUND: Taenia taeniaeformis and the related zoonotic cestode Echinococcus multilocularis both infect the water vole Arvicola terrestris. We investigated the effect of age, spatio-temporal and season-related factors on the prevalence of these parasites in their shared intermediate host. The absolute age of the voles was calculated based on their eye lens weights, and we included the mean day temperature and

The effects of incline (vertical versus horizontal) on spatio-temporal gait characteristics (stride and step length, frequency, duty factor, degree of sprawling) were measured over a range of speeds in a ground-dwelling (Eublepharis macularius) and a climbing (Gekko gecko) species of gecko. Surprisingly, the climbing species also performs very well when moving on the horizontal substratum. In the present experiments, climbing

Bromus tectorum (cheatgrass) is an invasive annual that occupies perennial grass and shrub communities throughout the western United States. Bromus tectorum exhibits an intriguing spatio-temporal pattern of invasion in low elevation ponderosa pine Pinus ponderosa\\/bunchgrass communities in western Montana where it forms dense rings beneath solitary pines following fire. This pattern provides a unique opportunity to investigate several indirect effects

827Spatio-Temporal Quantification of FRET in Living Cells by Fast Time-Domain FLIM: A Comparative´decine, Rennes, France Abstract Fo¨rster Resonance Energy Transfer (FRET) measured with Fluorescence Lifetime estimation of the FRET parameters requires a high number of photons and therefore long acquisition times

1 Estimating daily passive fish abundance in an open estuary from sparse data in spatio campaigns data coming from a sampling protocol which leads to spatio-temporal blanks in the fish distribution along the estuary and the time period that must be taken into account in fish abundance estimate

This paper introduces a method for visually exploring spatio?temporal data or predictions that come as probability density functions, e.g. output of statistical models or Monte Carlo simulations, under different scenarios. For a given moment in time, we can explore the probability dimension by looking at maps with cumulative or exceedance probability while varying the attribute level that is exceeded, or

Experimental results are presented for active vibration control of the Air Force Research Laboratory's UltraLITE Precision Deployable Optical Structure (PDOS), a ground based model of a sparse array, large aperture, deployable optical space telescope. The primary vibration suppression technique employs spatio-temporal filtering, in which a small number of sensors are used to produce modal coordinates for the structural modes to

Experimental results are presented for active vibration control of the Air Force Research Laboratory's UltraLITE Precision Deployable Optical Structure (PDOS), a ground based model of a sparse array, large aperture, deployable optical space telescope. The primary vibration suppression technique employs spatio-temporal filtering, in which a small number of sensors are used to produce modal coordinates for the structural modes to

Over the last decades, different machine learning techniques have been used to detect climate change patterns, mostly using data from measuring stations located in different parts of the world. Some previous studies focus on temperature as primary variable of study, though there have been other works focused on precipitation or even wind speed as objective variable. In this paper, we use the self-organized Second Order Data Coupled Clustering (SODCC) algorithm to carry out a spatio-temporal analysis of temperature patterns in Europe. By applying the SODCC we identify three different regimes of spatio-temporal correlations based on their geographical extent: small, medium, and large-scale regimes. Based on these regimes, it is possible to detect a change in the spatio-temporal trend of air temperature, reflecting a shift in the extent of the correlations in stations in the Iberian Peninsula and Southern France. We also identify an oscillating spatio-temporal trend in the Western Asia region and a stable medium-scale regime affecting the British Isles. These results are found to be consistent with previous studies in climate change. The patterns obtained with the SODCC algorithm may represent a signal of climate change to be taken into account, and so the SODCC could be used as detection method.

-temporal patterns of species assemblages SESAM ­ a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages Antoine Guisan1* & Carsten@bio.ku.dk ABSTRACT Two different approaches currently prevail for predicting spatial patterns of species assemblages

To develop effective strategies for managing biological invasions, it is important to understand and be able to predict patterns of invasion and range expansion, and particularly the rate of spread and factors controlling this rate. To predict the spatial dynamics of invasion by an alien bumblebee (Bombus terrestris) in Hokkaido, Japan, we explicitly constructed a stochastic spatio-temporal model that incorporates

Lung cancer is the second most commonly diagnosed cancer in both men and women in Georgia, USA. However, the spatio-temporal patterns of lung cancer risk in Georgia have not been fully studied. Hierarchical Bayesian models are used here to explore the spatio-temporal patterns of lung cancer incidence risk by race and gender in Georgia for the period of 2000-2007. With the census tract level as the spatial scale and the 2-year period aggregation as the temporal scale, we compare a total of seven Bayesian spatio-temporal models including two under a separate modeling framework and five under a joint modeling framework. One joint model outperforms others based on the deviance information criterion. Results show that the northwest region of Georgia has consistently high lung cancer incidence risk for all population groups during the study period. In addition, there are inverse relationships between the socioeconomic status and the lung cancer incidence risk among all Georgian population groups, and the relationships in males are stronger than those in females. By mapping more reliable variations in lung cancer incidence risk at a relatively fine spatio-temporal scale for different Georgian population groups, our study aims to better support healthcare performance assessment, etiological hypothesis generation, and health policy making.

UNDERDETERMINED BLINDSOURCE SEPARATION BASED ON GENERALIZED GAUSSIAN DISTRIBUTION SangGyun Kim mixtures of sources with both sub- and super- Gaussian distributions is proposed. In order to separate the sub- and super-Gaussian sources in the underdeterminedcase, generalized Gaussian distribution (GGD

This paper studies the blindsource separation (BSS) problem with the assumption that the source signals are cyclostationary. Identifiability and separability criteria based on second-order cyclostationary statistics (SOCS) alone are derived. The identifiability condition is used to define an appropriate contrast function. An iterative algorithm (ATH2) is derived to minimize this contrast function. This algorithm separates the sources even when

This paper considers the blind separation of audio sources in the underdetermined case, where we have more sources than sensors. A recent algorithm applies time-frequency distri bu- tions (TFDs) to this problem and gives good separation per- formance in the case where sources are disjoint in the time- frequency (TF) plane. However, in the non-disjoint case, the reconstruction of the

]--[12]. Such methods use higher­order statistical information about the source signals to iteratively adjustMULTICHANNEL BLIND SEPARATION AND DECONVOLUTION OF SOURCES WITH ARBITRARY DISTRIBUTIONS Scott C of arbitrary non­Gaussian sources. Our technique monitors the statistics of each of the outputs of the sepa

Although a crucial role of the fusiform gyrus (FG) in face processing has been demonstrated with a variety of methods, converging evidence suggests that face processing involves an interactive and overlapping processing cascade in distributed brain areas. Here we examine the spatio-temporal stages and their functional tuning to face inversion, presence and configuration of inner features, and face contour in healthy subjects during passive viewing. Anatomically-constrained magnetoencephalography (aMEG) combines high-density whole-head MEG recordings and distributed source modeling with high-resolution structural MRI. Each person's reconstructed cortical surface served to constrain noise-normalized minimum norm inverse source estimates. The earliest activity was estimated to the occipital cortex at ~100 ms after stimulus onset and was sensitive to an initial coarse level visual analysis. Activity in the right-lateralized ventral temporal area (inclusive of the FG) peaked at ~160 ms and was largest to inverted faces. Images containing facial features in the veridical and rearranged configuration irrespective of the facial outline elicited intermediate level activity. The M160 stage may provide structural representations necessary for downstream distributed areas to process identity and emotional expression. However, inverted faces additionally engaged the left ventral temporal area at ~180 ms and were uniquely subserved by bilateral processing. This observation is consistent with the dual route model and spared processing of inverted faces in prosopagnosia. The subsequent deflection, peaking at ~240 ms in the anterior temporal areas bilaterally, was largest to normal, upright faces. It may reflect initial engagement of the distributed network subserving individuation and familiarity. These results support dynamic models suggesting that processing of unfamiliar faces in the absence of a cognitive task is subserved by a distributed and interactive neural circuit. PMID:25426044

The majority of the area contaminated by the Fukushima Dai-ichi nuclear power plant accident is covered by forest. To facilitate effective countermeasure strategies to mitigate forest contamination, we simulated the spatio-temporal dynamics of radiocesium deposited into Japanese forest ecosystems in 2011 using a model that was developed after the Chernobyl accident in 1986. The simulation revealed that the radiocesium inventories in tree and soil surface organic layer components drop rapidly during the first two years after the fallout. Over a period of one to two years, the radiocesium is predicted to move from the tree and surface organic soil to the mineral soil, which eventually becomes the largest radiocesium reservoir within forest ecosystems. Although the uncertainty of our simulations should be considered, the results provide a basis for understanding and anticipating the future dynamics of radiocesium in Japanese forests following the Fukushima accident. PMID:23995073

To manage flood disaster with an exceeding designed level, flood risk control based on appropriate risk assessment is essential. To make an integrated economic risk assessment by flood disaster, a flood risk curve, which is a relation between flood inundation damage and its exceedance probability, plays an important role. This research purposes a method to develop a flood risk curve by utilizing a probability distribution function of annual maximum rainfall through rainfall-runoff and inundation simulations so that risk assessment can consider climate and socio-economic changes. Among a variety of uncertainties, the method proposed in this study considered spatio-temporal rainfall distributions that have high uncertainty for damage estimation. The method was applied to the Yura-gawa river basin (1882 km2) in Japan; and the annual economic benefit of an existing dam in the basin was successfully quantified by comparing flood risk curves with/without the dam.

Ocean circulation changes during the last glacial termination played an important role in atmospheric CO2 rise and millennial-scale climate change. These circulation changes can be reconstructed using benthic ?13C from Cibicidoides wuellerstorfi as a proxy for deep water geometry and ventilation. Here we analyze spatio-temporal variability in benthic ?13C across the deglaciation using data from ~90 core locations (70 from the Atlantic). We reconstruct patterns of ?13C change in 1-kyr timeslices from 2-24 kyr ago. Principal component analysis reveals the primary modes of deep water circulation change and their links to millennial-scale climate change. 3D visualization tools assist in the interpretation of benthic ?13C spatial patterns with respect to complex basin geometry (e.g. East vs. West Atlantic). Additionally, we present regional ?13C stacks that improve the signal-to-noise ratio for estimates of the mean ?13C change in different water masses through time.

Spontaneous pattern formation in a variety of spatially extended nonlinear system always occurs through a modulation instability: homogeneous state of the system becomes unstable with respect to growing modulation modes. Therefore, the manipulation of the modulation instability is of primary importance in controlling and manipulating the character of spatial patterns initiated by that instability. We show that the spatio-temporal periodic modulation of the potential of the spatially extended system results in a modification of its pattern forming instability. Depending on the modulation character the instability can be partially suppressed, can change its spectrum (for instance the long wave instability can transform into short wave instability), can split into two, or can be completely eliminated. The latter result is of especial practical interest, as can be used to stabilize the intrinsically unstable system. The result bears general character, as it is shown here on a universal model of Complex Ginzburg-L...

A hydrodynamic model of two-plasmon decay in a homogeneous plasma slab near the quarter-critical density is constructed in order to gain better insight into the spatio-temporal evolution of the daughter electron plasma waves in plasma in the course of the instability. The influence of laser and plasma parameters on the evolution of the amplitudes of the participating waves is discussed. The secondary coupling of two daughter electron plasma waves with an ion-acoustic wave is assumed to be the principal mechanism of saturation of the instability. The impact of the inherently nonresonant nature of this secondary coupling on the development of TPD is investigated and it is shown to significantly influence the electron plasma wave dynamics. Its inclusion leads to nonuniformity of the spatial profile of the instability and causes the burst-like pattern of the instability development, which should result in the burst-like hot-electron production in homogeneous plasma.

We propose a new method for joint segmentation of monotonously growing or shrinking shapes in a time sequence of noisy images. The task of segmenting the image time series is expressed as an optimization problem using the spatio-temporal graph of pixels, in which we are able to impose the constraint of shape growth or of shrinkage by introducing monodirectional infinite links connecting pixels at the same spatial locations in successive image frames. The globally optimal solution is computed with a graph cut. The performance of the proposed method is validated on three applications: segmentation of melting sea ice floes and of growing burned areas from time series of 2D satellite images, and segmentation of a growing brain tumor from sequences of 3D medical scans. In the latter application, we impose an additional intersequences inclusion constraint by adding directed infinite links between pixels of dependent image structures. PMID:25020092

A method for the quantitative assessment of spatio-temporal structuring of brain activity is presented. This approach is employed in a longitudinal case study of a child with frontal lobe epilepsy (FLE) and tested against an age-matched control group. Several correlation measures that are sensitive to linear and/or non-linear relations in multichannel scalp EEG are combined with an hierarchical cluster algorithm. Beside a quantitative description of the overall degree of synchronization the spatial relations are investigated by means of the cluster characteristics. The chosen information measures not only demonstrate their suitability in the characterization of the ictal and interictal phases but they also follow the course of delayed recovery of the psychiatric symptomatology during successful medication. The results based on this single case study suggest testing this approach for quantitative control of therapy in an extended clinical trial.

We show that a ring of unidirectionally delay-coupled spiking neurons may possess a multitude of stable spiking patterns and provide a constructive algorithm for generating a desired spiking pattern. More specifically, for a given time-periodic pattern, in which each neuron fires once within the pattern period at a predefined time moment, we provide the coupling delays and/or coupling strengths leading to this particular pattern. The considered homogeneous networks demonstrate a great multistability of various travelling time- and space-periodic waves which can propagate either along the direction of coupling or in opposite direction. Such a multistability significantly enhances the variability of possible spatio-temporal patterns and potentially increases the coding capability of oscillatory neuronal loops. We illustrate our results using FitzHugh-Nagumo neurons interacting via excitatory chemical synapses as well as limit-cycle oscillators.

A spatio-temporal metasurface is proposed to decompose in real time the temporal frequencies of electromagnetic waves into spatial frequencies onto a two-dimensional plane. The metasurface is analyzed and demonstrated using Fourier analysis. The required transmittance function is derived from an equivalent free-space optical system consisting of the cascade combination of a wedge, a diffraction grating and a focusing lens. The metasurface must exhibit both multi-resonance over a broad bandwidth and 1-D grating-type scanning to achieve the specified 2-D frequency scanning in space. Compared to state-of-the art related systems, the proposed metasurface system is more compact as it requires only one dispersive structure, while maintaining the high frequency resolution that characterizes 2-D spatial-temporal mapping systems.

The extent to which chromosomal domains are reorganized within the nucleus during differentiation is central to our understanding of how cells become committed to specific developmental lineages. Spatio-temporal patterns of DNA replication are a reflection of this organization. Here, we demonstrate that the temporal order and relative duration of these replication patterns during S-phase are similar in murine pluripotent embryonic stem (ES) cells, primary adult myoblasts, and an immortalized fibroblast line. The observed patterns were independent of fixation and denaturation techniques. Importantly, the same patterns were detected when fluorescent nucleotides were introduced into living cells, demonstrating their physiological relevance. These data suggest that heritable gene silencing during commitment to specific cell lineages is not mediated by global changes in the sub-nuclear organization and replication timing of chromosome domains. PMID:15723284

We describe a customizable and cost-effective light sheet microscopy (LSM) platform for rapid three-dimensional imaging of protein dynamics in small model organisms. The system is designed for high acquisition speeds and enables extended time-lapse in vivo experiments when using fluorescently labeled specimens. We demonstrate the capability of the setup to monitor gene expression and protein localization during ageing and upon starvation stress in longitudinal studies in individual or small groups of adult Caenorhabditis elegans nematodes. The system is equipped to readily perform fluorescence recovery after photobleaching (FRAP), which allows monitoring protein recovery and distribution under low photobleaching conditions. Our imaging platform is designed to easily switch between light sheet microscopy and optical projection tomography (OPT) modalities. The setup permits monitoring of spatio-temporal expression and localization of ageing biomarkers of subcellular size and can be conveniently adapted to image a wide range of small model organisms and tissue samples. PMID:26000610

We propose a spatial version of the neutral community model on a network of interconnected patches. The dynamical equations for the abundances and higher order moments of the abundances are established. Due to the neutrality assumption these equations are autonomous, enabling an exact analysis of spatio-temporal dynamics. We compute local (i.e., inside a patch) and global (i.e., between patches) diversities, and illustrate our results with two examples: (1) a non-spatial community, for which we recover previous results, and (2) a model with a finite number of patches which are all connected to each other with equal migration intensity. We discuss the relevance of this model for experiments in microbial ecology. PMID:19885659

This paper presents a robust adaptive embedding scheme using a modified Spatio-Temporal noticeable distortion (JND) model that is designed for tracing the distribution of the H.264/AVC video content and protecting them from unauthorized redistribution. The Embedding process is performed during coding process in selected macroblocks type Intra 4x4 within I-Frame. The method uses spread-spectrum technique in order to obtain robustness against collusion attacks and the JND model to dynamically adjust the embedding strength and control the energy of the embedded fingerprints so as to ensure their imperceptibility. Linear and non linear collusion attacks are performed to show the robustness of the proposed technique against collusion attacks while maintaining visual quality unchanged.

Designers of out-the-window visual systems face a challenge when attempting to simulate the outside world as viewed from a cockpit. Many methodologies have been developed and adopted to aid in the depiction of particular scene features, or levels of static image detail. However, because aircraft move, it is necessary to also consider the quality of the motion in the simulated visual scene. When motion is introduced in the simulated visual scene, perceptual artifacts can become apparent. A particular artifact related to image motion, spatiotemporal aliasing, will be addressed. The causes of spatio-temporal aliasing will be discussed, and current knowledge regarding the impact of these artifacts on both motion perception and simulator task performance will be reviewed. Methods of reducing the impact of this artifact are also addressed

The majority of the area contaminated by the Fukushima Dai-ichi nuclear power plant accident is covered by forest. To facilitate effective countermeasure strategies to mitigate forest contamination, we simulated the spatio-temporal dynamics of radiocesium deposited into Japanese forest ecosystems in 2011 using a model that was developed after the Chernobyl accident in 1986. The simulation revealed that the radiocesium inventories in tree and soil surface organic layer components drop rapidly during the first two years after the fallout. Over a period of one to two years, the radiocesium is predicted to move from the tree and surface organic soil to the mineral soil, which eventually becomes the largest radiocesium reservoir within forest ecosystems. Although the uncertainty of our simulations should be considered, the results provide a basis for understanding and anticipating the future dynamics of radiocesium in Japanese forests following the Fukushima accident. PMID:23995073

The vertebrate retina is very well conserved in evolution. Its structure and functional features are very similar in phyla as different as primates and teleost fish. Here we describe the spatio-temporal characteristics of neurogenesis in the retina of a teleost, medaka, and compare them to other species, primarily the zebrafish. Several intriguing differences are observed between medaka and zebrafish. For example, photoreceptor differentiation in the medaka retina starts independently in two different areas, and at more advanced stages of differentiation, medaka and zebrafish retinae display obviously different patterns of the photoreceptor cell mosaic. Medaka and zebrafish evolutionary lineages are thought to have separated from each other 110 million years ago, and so the differences between these species are not unexpected, and may be exploited to gain insight into the architecture of developmental pathways. Importantly, this work highlights the benefits of using multiple teleost models in parallel to understand a developmental process. PMID:19035349

Mathematical models of marine populations exhibit chaotic dynamics. However, we hypothesize that in moving water, Eulerian sampling of spatially heterogeneous populations may obscure any deterministic signal beyond the resolving capabilities of presently available nonlinear signal processing techniques. To examine this hypothesis we created two spatio-temporal models of population dynamics. To caricature actual ocean sampling limitations, we sampled the model output in two ways, random walks to simulate Eulerian sampling, and spatial averages to simulate population measurements from finite volumes. Results indicate that the ability to identify underlying nonlinear dynamics quickly degrades as the step size of a random walk sampling increases. On the other hand, the analysis techniques used are more robust in the face of spatial averaging.

The unconventional emergency, usually outbreaks more suddenly, and is diffused more quickly, but causes more secondary damage and derives more disaster than what it is usually expected. The data volume and urgency of emergency exceeds the capacity of current emergency management systems. In this paper, we propose a three-tier collaborative spatio-temporal visual analysis architecture to support emergency management. The prototype system, based on cloud computation environment, supports aggregation of massive unstructured and semi-structured data, integration of various computing model sand algorithms; collaborative visualization and visual analytics among users with a diversity of backgrounds. The distributed data in 100TB scale is integrated in a unified platform and shared with thousands of experts and government agencies by nearly 100 models. The users explore, visualize and analyse the big data and make a collaborative countermeasures to emergencies.

We review the psychophysics of the spatio-temporal contrast sensitivity in the cardinal directions of the colour space and their correlation with those neural characteristics of the visual system that limit the ability to perform contrast detection or pattern-resolution tasks. We focus our attention particularly on the influence of luminance level, spatial extent and spatial location of the stimuli - factors that determine the characteristics of the physiological mechanisms underlying detection. Optical factors do obviously play a role, but we will refer to them only briefly. Contrast sensitivity measurements are often used in clinical practice as a method to detect, at their early stages, a variety of pathologies affecting the visual system, but their usefulness is very limited due to several reasons. We suggest some considerations about stimuli characteristics that should be taken into account in order to improve the performance of this kind of measurement.

We propose a new method for joint segmentation of monotonously growing or shrinking shapes in a time sequence of noisy images. The task of segmenting the image time series is expressed as an optimization problem using the spatio-temporal graph of pixels, in which we are able to impose the constraint of shape growth or of shrinkage by introducing monodirectional infinite links connecting pixels at the same spatial locations in successive image frames. The globally optimal solution is computed with a graph cut. The performance of the proposed method is validated on three applications: segmentation of melting sea ice floes and of growing burned areas from time series of 2D satellite images, and segmentation of a growing brain tumor from sequences of 3D medical scans. In the latter application, we impose an additional intersequences inclusion constraint by adding directed infinite links between pixels of dependent image structures.

Flavonoids are important chemicals of resistance to pests in cotton plant. The main flavonoid chemicals and their spatio-temporal dynamics of content in Bt transgenic cotton were tested by HPLC. The results showed that the flavonoid chemicals of resistance to pests mainly including rutin, isoquercitrin and quercetin could be detected and quantitatively analyzed by HPLC. The contents of rutin, isoquercitrin and quercetin were the highest in petal, but lower in calyx, bract and cotton boll. Moreover, the total content of flavonoid chemicals in top leaf was much higher at developmental stage than at seedling stage. The content of each flavonoid chemicals of resistance to pests was different during different developmental stage and in different organs. It was indicated that different flavonoid chemicals played different roles in resistance to pests. PMID:12827880

The Sensor Web is a macroinstrument concept that allows for the spatio-temporal understanding of an environment through coordinated efforts between multiple numbers and types of sensing platforms, including, in its most general form, both orbital and terrestrial and both fixed and mobile. Each of these platforms, or pods, communicates within its local neighborhood and thus distributes information to the instrument as a whole. The result of sharing and continual processing of this information among all the Sensor Web elements will result in an information flow and a global perception of and reactive capability to the environment. As illustrated, the Sensor Web concept also allows for the recursive notion of a web of webs with individual distributed instruments possibly playing the role of a single node point on a larger Sensor Web instrument. In particular, the fusion of inexpensive, yet sophisticated, commercial technology from both the computation and telecommunication revolutions has enabled the development of practical, fielded, and embedded in situ systems that have been the focus of the NASA/JPL Sensor Webs Project (http://sensorwebs.jpl.nasa.gov/). These Sensor Webs are complete systems consisting of not only the pod elements that wirelessly communicate among themselves, but also interfacing and archiving software that allows for easy use by the end-user. Previous successful deployments have included environments as diverse as coastal regions, Antarctica, and desert areas. The Sensor Web has broad implications for Earth and planetary science and will revolutionize the way experiments and missions are conceived and performed. As part of our current efforts to develop a macrointelligence within the system, we have deployed a Sensor Web at the Central Avra Valley Storage and Recovery Project (CAVSARP) facility located west of Tucson, AZ. This particular site was selected because it is ideal for studying spatio-temporal phenomena and for providing a test site for more sophisticated hydrological studies in the future.

The North Sea cod (Gadus morhua, L.) stock has continuously declined over the past four decades linked with overfishing and climate change. Changes in stock structure due to overfishing have made the stock largely dependent on its recruitment success, which greatly relies on environmental conditions. Here we focus on the spatio-temporal variability of cod recruitment in an effort to detect changes during the critical early life stages. Using International Bottom Trawl Survey (IBTS) data from 1974 to 2011, a major spatio-temporal change in the distribution of cod recruits was identified in the late 1990s, characterized by a pronounced decrease in the central and southeastern North Sea stock. Other minor spatial changes were also recorded in the mid-1980s and early 1990s. We tested whether the observed changes in recruits distribution could be related with direct (i.e. temperature) and/or indirect (i.e. changes in the quantity and quality of zooplankton prey) effects of climate variability. The analyses were based on spatially-resolved time series, i.e. sea surface temperature (SST) from the Hadley Center and zooplankton records from the Continuous Plankton Recorder Survey. We showed that spring SST increase was the main driver for the most recent decrease in cod recruitment. The late 1990s were also characterized by relatively low total zooplankton biomass, particularly of energy-rich zooplankton such as the copepod Calanus finmarchicus, which have further contributed to the decline of North Sea cod recruitment. Long-term spatially-resolved observations were used to produce regional distribution models that could further be used to predict the abundance of North Sea cod recruits based on temperature and zooplankton food availability. PMID:24551103

The theory that forests become carbon (C) neutral with maturity has recently been challenged. While a growing body of evidence shows that net C accumulation continues in forests that are centuries old, the reasons remain poorly known. Increasing canopy structural complexity, quantified by high variability in leaf distribution, has been proposed as a mechanism for sustained rates of C assimilation in mature forests. The goal of our research was to expand on these findings and explore a new idea of spatio-temporal canopy structural complexity as a mechanism linking canopy structure to function (C assimilation).Our work takes place at the UMBS AmeriFlux core facility (US-UMB) in northern Michigan, USA. Canopy structure was quantified over 6 seasons with portable canopy LiDAR (PCL) and canopy spatial microhabitat variability was studied using hemispherical photographs from different heights within the canopy. We found a more even distribution of irradiance in more structurally complex canopies within a single year, and furthermore, that between-year variability of spatial leaf arrangement decreased with increasing canopy complexity. We suggest that in complex canopies less redistribution of leaf material over time may lead to more similar light microhabitats within and among years. Conversely, in less complex canopies this relationship can lead to a year-to-year time lag in morphological leaf acclimation since the effects of the previous-year's light environment are reflected in the morphological characteristics of current-year leaves.Our study harnesses unique spatio-temporal resolution measurements of canopy structure and microhabitat that can inform better management strategies seeking to maximize forest C uptake. Future research quantifying the relationship between canopy structure and light distribution will improve performance of ecosystem models that currently lack spatially explicit canopy structure information.

The municipal, spatial pattern of male stomach cancer mortality in Spain, spanning the period 1989-2008, was studied, comparing the results of depicting mortality using different expected-case computation methods in a spatial and spatio- temporal modelling context. Expected cases for each municipality were first calculated by two methods: (i) using reference rates for each 5-year period; and (ii) using average reference rates for the overall period. This was visualised by two types of models: (i) independent maps for each period based on the model proposed by Besag, York and Mollié; and (ii) a series of maps over time based on a model with spatio-temporal interaction terms. An additional model, based on mortality rate ratios as an alternative to the traditional use of standardised mortality ratios, was also fitted. Integrated nested Laplace approximations were used as the Bayesian inference tool. The results show that, in general, the geographical pattern was maintained across the study period, and that the maps differed appreciably according to the method used to obtain the expected number of cases. While the use of average reference rates appears to be the most suitable choice where the aim is to study time trends by area, it may nevertheless mask the spatial pattern in situations where the time trend is very marked and the study period is long. When it comes to studying changes in the spatial pattern of stomach cancer mortality, we feel that it is most useful to plot independent maps by period and use the "local" rates for each period as reference in the computation of expected cases. PMID:25545923

Although the consequences of floods are strongly related to their peak discharges, a statistical classification of flood events that only depends on these peaks may not be sufficient for flood risk assessments. In many cases, the flood risk depends on a number of event characteristics. In case of an extreme flood, the whole river basin may be affected instead of a single watershed, and there will be superposition of peak discharges from adjoining catchments. These peaks differ in size and timing according to the spatial distribution of precipitation and watershed-specific processes of flood formation. Thus, the spatial characteristics of flood events should be considered as stochastic processes. Hence, there is a need for a multivariate statistical approach that represents the spatial interdependencies between floods from different watersheds and their coincidences. This paper addresses the question how these spatial interdependencies can be quantified. Each flood event is not only assessed with regard to its local conditions but also according to its spatio-temporal pattern within the river basin. In this paper we characterise the coincidence of floods by trivariate Joe-copula and pair-copulas. Their ability to link the marginal distributions of the variates while maintaining their dependence structure characterizes them as an adequate method. The results indicate that the trivariate copula model is able to represent the multivariate probabilities of the occurrence of simultaneous flood peaks well. It is suggested that the approach of this paper is very useful for the risk-based design of retention basins as it accounts for the complex spatio-temporal interactions of floods.

This is the second of a sequence of two presentations concerned with understanding the nature and generation mechanisms of the unsteady surface pressure in impinging jet flows. In the first presentation, the mechanisms influencing the evolution of the surface pressure are studied by examining instantaneous realizations obtained from time-resolved flow visualization and concurrent surface-embedded microphone array measurements; along with numerical simulations of related model problems. In this presentation, the focus is on examining the statistical importance and persistence of these mechanisms by comparing knowledge obtained from the instantaneous analysis to that resulting from inspection of conditional spatio-temporal surface-pressure behaviors, frequency-wavenumber spectra and other statistical measures. Results are presented for surface-pressure measurements at a Reynolds number based on jet diameter of approximately 7000. Dependence of the results on the spacing between the impingement wall and the jet as well as the jet impingement angle is also considered. This is the second of a sequence of two presentations concerned with understanding the nature and generation mechanisms of the unsteady surface pressure in impinging jet flows. In the first presentation, the mechanisms influencing the evolution of the surface pressure are studied by examining instantaneous realizations obtained from time-resolved flow visualization and concurrent surface-embedded microphone array measurements; along with numerical simulations of related model problems. In this presentation, the focus is on examining the statistical importance and persistence of these mechanisms by comparing knowledge obtained from the instantaneous analysis to that resulting from inspection of conditional spatio-temporal surface-pressure behaviors, frequency-wavenumber spectra and other statistical measures. Results are presented for surface-pressure measurements at a Reynolds number based on jet diameter of approximately 7000. Dependence of the results on the spacing between the impingement wall and the jet as well as the jet impingement angle is also considered. Partly funded by NSF grant OISE-0611984 and Libyan-North American Scholarship program.

It is often difficult to track the spatio-temporal variability of vegetation distribution in lakes because of the technological limitations associated with mapping using traditional field surveys as well as the lack of a unified field survey protocol. Using a series of Landsat remote sensing images (i.e. MSS, TM and ETM+), we mapped the composition and distribution area of emergent, floating-leaf and submerged macrophytes in Taihu Lake, China, at approximate five-year intervals over the past 30 years in order to quantify the spatio-temporal dynamics of the aquatic vegetation. Our results indicated that the total area of aquatic vegetation increased from 187.5 km(2) in 1981 to 485.0 km(2) in 2005 and then suddenly decreased to 341.3 km(2) in 2010. Similarly, submerged vegetation increased from 127.0 km(2) in 1981 to 366.5 km(2) in 2005, and then decreased to 163.3 km(2). Floating-leaf vegetation increased continuously through the study period in both area occupied (12.9 km(2) in 1981 to 146.2 km(2) in 2010) and percentage of the total vegetation (6.88% in 1981 to 42.8% in 2010). In terms of spatial distribution, the aquatic vegetation in Taihu Lake has spread gradually from the East Bay to the surrounding areas. The proportion of vegetation in the East Bay relative to that in the entire lake has decreased continuously from 62.3% in 1981, to 31.1% in 2005 and then to 21.8% in 2010. Our findings have suggested that drastic changes have taken place over the past 30 years in the spatial pattern of aquatic vegetation as well as both its relative composition and the amount of area it occupies. PMID:23823189

The liver is the central organ for detoxification of xenobiotics in the body. In pharmacokinetic modeling, hepatic metabolization capacity is typically quantified as hepatic clearance computed as degradation in well-stirred compartments. This is an accurate mechanistic description once a quasi-equilibrium between blood and surrounding tissue is established. However, this model structure cannot be used to simulate spatio-temporal distribution during the first instants after drug injection. In this paper, we introduce a new spatially resolved model to simulate first pass perfusion of compounds within the naive liver. The model is based on vascular structures obtained from computed tomography as well as physiologically based mass transfer descriptions obtained from pharmacokinetic modeling. The physiological architecture of hepatic tissue in our model is governed by both vascular geometry and the composition of the connecting hepatic tissue. In particular, we here consider locally distributed mass flow in liver tissue instead of considering well-stirred compartments. Experimentally, the model structure corresponds to an isolated perfused liver and provides an ideal platform to address first pass effects and questions of hepatic heterogeneity. The model was evaluated for three exemplary compounds covering key aspects of perfusion, distribution and metabolization within the liver. As pathophysiological states we considered the influence of steatosis and carbon tetrachloride-induced liver necrosis on total hepatic distribution and metabolic capacity. Notably, we found that our computational predictions are in qualitative agreement with previously published experimental data. The simulation results provide an unprecedented level of detail in compound concentration profiles during first pass perfusion, both spatio-temporally in liver tissue itself and temporally in the outflowing blood. We expect our model to be the foundation of further spatially resolved models of the liver in the future. PMID:24625393

Combined Global Surface Summary of Day and European Climate Assessment and Dataset daily meteorological data sets (around 9000 stations) were used to build spatio-temporal geostatistical models and predict daily air temperature at ground resolution of 1 km for the global land mass. Predictions in space and time were made for the mean, maximum, and minimum temperatures using spatio-temporal regression-kriging with a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) 8 day images, topographic layers (digital elevation model and topographic wetness index), and a geometric temperature trend as covariates. The accuracy of predicting daily temperatures was assessed using leave-one-out cross validation. To account for geographical point clustering of station data and get a more representative cross-validation accuracy, predicted values were aggregated to blocks of land of size 500×500 km. Results show that the average accuracy for predicting mean, maximum, and minimum daily temperatures is root-mean-square error (RMSE) =±2°C for areas densely covered with stations and between ±2°C and ±4°C for areas with lower station density. The lowest prediction accuracy was observed at high altitudes (>1000 m) and in Antarctica with an RMSE around 6°C. The model and predictions were built for the year 2011 only, but the same methodology could be extended for the whole range of the MODIS land surface temperature images (2001 to today), i.e., to produce global archives of daily temperatures (a next-generation http://WorldClim.org repository) and to feed various global environmental models.

The paper discusses blindsource recovery (BSR) of minimum phase and non-minimum phase mixtures of multiple source distributions using an adaptive score function. This proposed parametric score function is derived from the generalized Gaussian distribution model. An adaptive algorithm to determine the tuning parameter for the proposed score function using batch kurtosis of the BSR output is also presented. The

In this paper, we address the convolutive blindsource sep- aration (BSS) problem with a sparse independent component analysis (ICA) method, which uses ICA to flnd a set of basis vectors from the observed data, followed by clustering to identify the original sources. We show that, thanks to the temporally localised basis vectors that result, phase information is easily exploited

We present a real-time version of the DUET algorithm for the blind separation of any number of sources using only two mixtures. The method applies when sources are W- disjoint orthogonal, that is, when the supports of the win- dowed Fourier transform of any two signals in the mixture are disjoint sets, an assumption which is justified in the Ap-

The information about lithospheric deformations may be obtained nowadays by analysis of velocity field derived from permanent GNSS (Global Navigation Satellite System) observations. Despite developing more and more reliable models, the permanent stations residuals must still be considered as coloured noise. Meeting the GGOS (Global Geodetic Observing System) requirements, we are obliged to investigate the correlations between residuals, which are the result of common mode error (CME). This type of error may arise from mismodelling of: satellite orbits, the Earth Orientation Parameters, satellite antenna phase centre variations or unmodelling of large scale atmospheric effects. The above described together cause correlations between stochastic parts of coordinate time series obtained at stations located of even few thousands kilometres from each other. Permanent stations that meet the aforementioned terms form the regional (EPN - EUREF Permanent Network) or local sub-networks of global (IGS - International GNSS Service) network. Other authors (Wdowinski et al., 1997; Dong et al., 2006) dealt with spatio-temporal filtering and indicated three major regional filtering approaches: the stacking, the Principal Component Analysis (PCA) based on the empirical orthogonal function and the Karhunen-Loeve expansion. The need for spatio-temporal filtering is evident today, but the question whether the size of the network affects the accuracy of station's position and its velocity still remains unanswered. With the aim to determine the network's size, for which the assumption of spatial uniform distribution of CME is retained, we used stacking approach. We analyzed time series of IGS stations with daily network solutions processed by the Military University of Technology EPN Local Analysis Centre in Bernese 5.0 software and compared it with the JPL (Jet Propulsion Laboratory) PPP (Precice Point Positioning). The method we propose is based on the division of local GNSS networks into concentric ring-shaped areas. Such an approach allows us to specify the maximum size of the network, where the evident uniform spatial response can be still noticed. In terms of reliable CMEs extraction, the local networks have to be up to 500-600 kilometres extent depending on its character (location). In this study we examined three approaches of spatio-temporal filtering based on stacking procedure. First was based on non-weighted (Wdowinski et. al., 1997) and second on weighted average formula, where the weights are formed by the RMS of individual station position in the corresponding epoch (Nikolaidis, 2002). The third stacking approach, proposed here, was previously unused. It combines the weighted stacking together with the distance between the station and network barycentre into one approach. The analysis allowed to determine the optimal size of local GNSS network and to select the appropriate stacking method for obtaining the most stable solutions for e.g. geodynamical studies. The values of L1 and L2 norms, RMS values of time series (describing stability of the time series) and Pearson correlation coefficients were calculated for the North, East and Up components from more than 200 permanent stations twice: before performing the filtration and after weighted stacking approach. We showed the improvement in the quality of time series analysis using MLE (Maximum Likelihood Estimation) to estimate noise parameters. We demonstrated that the relative RMS improvement of 10, 20 and 30% reduces the noise amplitudes of about 20, 35 and 45%, respectively, what causes the velocity uncertainty to be reduced of 0.3 mm/yr (for the assumption of 7-years of data and flicker noise). The relative decrement of spectral index kappa is 25, 35 and 45%, what means lower velocity uncertainty of even 0.2 mm/yr (when assuming 7 years of data and noise amplitude of 15 mm/yr^-kappa/4) . These results refer to the growing demands on the stability of the series due to their use to realize the kinematic reference frames and for geodynamical studies.

When sampling spatio-temporal random variables, the cost of a measurement may differ according to the setup of the whole sampling design: static measurements, i.e. repeated measurements at the same location, synchronous measurements or clustered measurements may be cheaper per measurement than completely individual sampling. Such "grouped" measurements may however not be as good as individually chosen ones because of redundancy. Often, the overall cost rather than the total number of measurements is fixed. A sampling design with grouped measurements may allow for a larger number of measurements thus outweighing the drawback of redundancy. The focus of this paper is to include the tradeoff between the number of measurements and the freedom of their location in sampling design optimisation. For simple cases, optimal sampling designs may be fully determined. To predict e.g. the mean over a spatio-temporal field having known covariance, the optimal sampling design often is a grid with density determined by the sampling costs [1, Ch. 15]. For arbitrary objective functions sampling designs can be optimised relocating single measurements, e.g. by Spatial Simulated Annealing [2]. However, this does not allow to take advantage of lower costs when using grouped measurements. We introduce a heuristic that optimises an arbitrary objective function of sampling designs, including static, synchronous, or clustered measurements, to obtain better results at a given sampling budget. Given the cost for a measurement, either within a group or individually, the algorithm first computes affordable sampling design configurations. The number of individual measurements as well as kind and number of grouped measurements are determined. Random locations and dates are assigned to the measurements. Spatial Simulated Annealing is used on each of these initial sampling designs (in parallel) to improve them. In grouped measurements either the whole group is moved or single measurements within the group, e.g. static measurements may be moved to another location or the sampling times may be rearranged. After several optimisation steps, the objective functions of the sampling designs are compared. Only for the best ones optimisation is pursued. After several iterations the sampling designs are selected again. Thus more and more of the low performing sampling designs are deleted and computational effort is concentrated on the most promising candidates. The use case is optimisation of a monitoring sampling design for a river. We use a flow model to simulate the spread of a pollutant that enters the system at different locations with known, location-dependent probabilities and at random times. The objective function to be minimised is the amount of pollution that is not detected. Keywords: spatio-temporal sampling design, static sample, synchronous sample, spatial simulated annealing, cost function References [1] Jaap de Gruijter, Dick Brus, Marc Bierkens, and Martin Knotters. Sampling for Natural Ressource Monitoring. Springer, 2006. [2] J. W. van Groenigen. Spatial simulated annealing for optimizing sampling, In: GeoENV I Geostatistics for environmental applications, pages 351 - 361, 1997.

Abstract We apply a type of generative modelling to the problem,of blindsource separation in which prior knowledge about the latent source signals, such as time-varying auto-correlation and quasi- periodicity, are incorporated into a linear state-space model. In simulations, we show that in terms of signal-to-error ratio, the sources are inferred more accurately as a result of the inclusion of

A study on the spatio-temporal dynamics of broad-area Nd:YAG (yttrium aluminium garnet), Nd-doped phosphate glass and Nd-doped silicate glass lasers is presented to show the influence of the inhomogeneous gain profile and cross-relaxation phenomena on the spatio-temporal dynamics of the system. The suppression of the order-disorder transition shown in Cabrera et al (2006 Opt. Lett. 31 1067) for homogeneously broadened class B lasers is found for both glass lasers, independently of the strength of the cross-relaxation mechanisms. The results obtained indicate that a higher degree of inhomogeneous broadening leads to suppression of the filamentation in the transverse intensity pattern.

It has been known that human being exhibits the Fight or Flight Reaction(FFR) when they feel anxiety, strain and threat. This paper describes experiments that were conducted to arouse the fight or flight reaction. Facial skin thermograms in which the temperature fluctuation in specific regions was identified were measured, and the characteristics of the temperature fluctuations in the relevant regions were quantitatively evaluated. The results showed that, for nine of the ten subjects, the FFR was confirmed in the form of reacted areas indicating acute increases in skin temperature, primarily in facial expression muscles such as the procerus muscle and cheek muscles. Additionally, the spatio-temporal spectrum differential analysis method for facial skin thermograms was proposed, and as a result of detecting spatio-temporal skin temperature fluctuations in the facial skin thermograms accompanying manifestation of the FFR, a detection rate of 76.5% was obtained. Thus, the effectiveness of the proposed technique was confirmed.

Inferior olive (IO) neurons project to the cerebellum and contribute to motor control. They can show intriguing spatio-temporal dynamics with rhythmic and synchronized spiking. IO neurons are connected to their neighbors via gap junctions to form an electrically coupled network, and so it is considered that this coupling contributes to the characteristic dynamics of this nucleus. Here, we demonstrate that a gap junction-coupled network composed of simple conductance-based model neurons (a simplified version of a Hodgkin–Huxley type neuron) reproduce important aspects of IO activity. The simplified phenomenological model neuron facilitated the analysis of the single cell and network properties of the IO while still quantitatively reproducing the spiking patterns of complex spike activity observed by simultaneous recording in anesthetized rats. The results imply that both intrinsic bistability of each neuron and gap junction coupling among neurons play key roles in the generation of the spatio-temporal dynamics of IO neurons. PMID:21637736

Enhancing the well known and widely used map algebra proposed by Dr. Charles Dana Tomlin [1] with the time dimension is an ongoing research topic. The efficient processing of large time series of raster, 3D raster and vector datasets, e. g. raster datasets for temperature or precipitations on continental scale, requires a sophisticated spatio-temporal algebra that is capable of handling datasets with different temporal granularities and spatio-temporal extents. With the temporal enabled GRASS GIS [2] and the GRASS GIS Temporal Framework new spatio-temporal data types are available in GRASS GIS 7, called space time datasets. These space time datasets represent time series of raster, 3D raster and vector map layers. Furthermore the temporal framework provides a wide range of functionalities to support the implementation of a temporal algebra. While spatial capabilities of GRASS GIS are used to perform the spatial processing of the time stamped map layers that are registered in a space time dataset, the temporal processing is provided by the GRASS GIS temporal framework that supports time intervals and time instances. Mixing time instance and time intervals as well as gaps, overlapping or inclusion of intervals and instances is possible. Hence this framework allows an arbitrary layout of the time dimension. We implemented two ways to process space time datasets with arbitrary temporal layout, the temporal topology and the granularity based spatio-temporal algebra. The algebra provides the functionality to define complex spatio-temporal topological operators that process time and space in a single expression. The algebra includes methods to select map layers from space time datasets based on their temporal relations, to temporally shift time stamped map layers, to create temporal buffer and to snap time instances of time stamped map layers to create a valid temporal topology. In addition spatio-temporal operations can be evaluated within conditional statements. These operations can be assigned to space time datasets or to the results of operations between space time datasets. The temporal vector algebra adds spatial overlay and buffer operations that can be performed on temporal related vector map layers that are registered in space time vector datasets. Whereas the temporal raster and 3D raster algebra uses a subset of the arithmetic operators and spatial functions from the raster algebra in GRASS GIS. It provides in addition spatio-temporal neighborhood operators and spatio-temporal functions. All operations between multiple space time datasets can be combined in nested expressions and are preprocessed by meta data topology analysis before the relevant expressions are computed with parallel processing. [1] Tomlin, C. Dana., 1990. Geographic Information Systems and Cartographic Modeling. Englewood Cliffs, NJ: Prentice-Hall. [2] Gebbert, S., Pebesma, E., 2014. A temporal GIS for field based environmental modeling. Environ. Model. Softw. 53, 1-12.

Understanding the dynamics of neural networks is a major challenge in experimental neuroscience. For that purpose, a modelling of the recorded activity that reproduces the main statistics of the data is required. In the first part, we present a review on recent results dealing with spike train statistics analysis using maximum entropy models (MaxEnt). Most of these studies have focused on modelling synchronous spike patterns, leaving aside the temporal dynamics of the neural activity. However, the maximum entropy principle can be generalized to the temporal case, leading to Markovian models where memory effects and time correlations in the dynamics are properly taken into account. In the second part, we present a new method based on Monte Carlo sampling which is suited for the fitting of large-scale spatio-temporal MaxEnt models. The formalism and the tools presented here will be essential to fit MaxEnt spatio-temporal models to large neural ensembles.

In recent years source separation has become an increasingly popular area of research in the signal processing community. The subject has found applications in a variety of fields such as medical imaging, sound and audio, econometrics and geophysics. This document will discuss the application of source separation techniques to the area of audio. Sound source separation is the process of

Schistosomiasis is a water-borne parasitic disease endemic in tropical and subtropical areas. Its transmission requires certain kind of snail as the intermediate host. Some efforts have been made to mapping snail habitats with remote sensing and schistosomiasis transmission modeling. However, the modeling is limited to isolated residential groups and does not include spatial interaction among those groups. Remotely sensed data are only used in snail habitat classification, not in estimation of snail abundance that is an important parameter in schistosomiasis transmission modeling. This research overcomes the above two problems using innovative geographic information system (GIS) and remote sensing technology. A mountainous environment near Xichang, China, is chosen as the test site. Environmental and epidemiological data are stored in a GIS to support modeling. Snail abundance is estimated from land-cover and land-use fractions derived from high spatial resolution IKONOS satellite data. Spatial interaction is determined in consideration of neighborhoods, group areas, relative slopes among groups, and natural barriers. Land-cover and land-use information extracted from 4 m high resolution IKONOS data is used as reference in scaling up to the regional level. The scale-up is done with coarser resolution satellite data including Landsat Thematic Mapper (TM), EO-1 Advanced Land Imager (ALI) and Hyperion data all at 30 m resolution. Snail abundance is estimated by regressing snail survey data with land-cover and land-use fractions. An R2 of 0.87 is obtained between the average snail density predicted and that surveyed at the group level. With such a model, a snail density map is generated for all residential groups in the study area. A spatio-temporal model of schistosomiasis transmission is finally built to incorporate the spatial interaction caused by miracidia and cercaria migration. Comparing the model results with and without spatial interaction has revealed a number of advantages of the spatio-temporal model. Particularly, with the inclusion of spatial interaction, more effective control of schistosomiasis transmission over the whole study area can be achieved.

Background Tuberculosis (TB) is a disease of public health concern, with a varying distribution across settings depending on socio-economic status, HIV burden, availability and performance of the health system. Ethiopia is a country with a high burden of TB, with regional variations in TB case notification rates (CNRs). However, TB program reports are often compiled and reported at higher administrative units that do not show the burden at lower units, so there is limited information about the spatial distribution of the disease. We therefore aim to assess the spatial distribution and presence of the spatio-temporal clustering of the disease in different geographic settings over 10 years in the Sidama Zone in southern Ethiopia. Methods A retrospective space–time and spatial analysis were carried out at the kebele level (the lowest administrative unit within a district) to identify spatial and space-time clusters of smear-positive pulmonary TB (PTB). Scan statistics, Global Moran’s I, and Getis and Ordi (Gi*) statistics were all used to help analyze the spatial distribution and clusters of the disease across settings. Results A total of 22,545 smear-positive PTB cases notified over 10 years were used for spatial analysis. In a purely spatial analysis, we identified the most likely cluster of smear-positive PTB in 192 kebeles in eight districts (RR= 2, p<0.001), with 12,155 observed and 8,668 expected cases. The Gi* statistic also identified the clusters in the same areas, and the spatial clusters showed stability in most areas in each year during the study period. The space-time analysis also detected the most likely cluster in 193 kebeles in the same eight districts (RR= 1.92, p<0.001), with 7,584 observed and 4,738 expected cases in 2003-2012. Conclusion The study found variations in CNRs and significant spatio-temporal clusters of smear-positive PTB in the Sidama Zone. The findings can be used to guide TB control programs to devise effective TB control strategies for the geographic areas characterized by the highest CNRs. Further studies are required to understand the factors associated with clustering based on individual level locations and investigation of cases. PMID:26030162

In recent years, large multifaceted spatial, temporal, and spatio-temporal databases have attained significant popularity and importance in the database community. In order to perform preliminary investigation, exploratory visual analysis of such data-sets is highly desirable. To facilitate the convenient and efficient visualization, scientists and practitioners often need to convert the spatial component of the data-set into a more usable format.

The monitoring of a set of individual neurons in cultured biological networks or in the brain has become feasible with the used\\/development of multi-electrode arrays (MEA). However, even with the huge mass of data, getting suitable information about the actual spatio-temporal context of the analyzed biological network is not easy. In this paper we present a new conception and first

This paper is devoted to imaging defects in liquid and solid ultrasonic waveguides. A new ultrasonic imaging technique, based on the spatio-temporal Green functions computation and cross-correlation, is presented. This technique extends the concept of matched field processing (MFP) used in ocean acoustics. Results of experiments conducted in water and in a solid Duralumin bar show that a strong improvement of the spatial resolution is observed with this MFP. PMID:11370351

Cadherin-dependent epithelial cell-cell adhesion is thought to be regulated by Rho family small GTPases and PI 3-kinase, but the mechanisms involved are poorly understood. Using time-lapse microscopy and quantitative image analysis, we show that cell-cell contact in MDCK epithelial cells coincides with a spatio-temporal reorganization of plasma membrane Rac1 and lamellipodia from noncontacting to contacting surfaces. Within contacts, Rac1 and

The spatio-temporal distribution of lightning flashes over Israel and the neighboring area and its relation to the regional synoptic systems has been studied, based on data obtained from the Israel Lightning Location System (ILLS) operated by the Israel Electric Corporation (IEC). The system detects cloud-to-ground lightning discharges in a range of ~500 km around central Israel (32.5° N, 35° E).

Spatio-temporal variations of lifetime reproductive succes (LRS) of both male and female individuals of a coreid bugColpula lativentris were measured and analyzed using the multiple regression method of Arnold and Wade (1984a, b). The standardized variance\\u000a of LRS was larger in males than that in females as males often to secure mates for a long period whereas females could easily

This paper presents a view-invariant approach to gait recognition in multi-camera scenarios exploiting a joint spatio-temporal data representation and analysis. First, multi-view information is employed to generate a 3D voxel reconstruction of the scene under study. The analyzed subject is tracked and its centroid and orientation allow recentering and aligning the volume associated to it, thus obtaining a representation invariant

Job scheduling in data centers can be considered from a cyber-physical point of view, as it aects the data center's computing per- formance (i.e. the cyber aspect) and energy eciency (the physical aspect). Driven by the growing needs to green contemporary data centers, this paper uses recent technological advances in data center virtualization and proposes cyber-physical, spatio-temporal (i.e. start time

Job scheduling in data centers can be considered from a cyber–physical point of view, as it affects the data center’s computing performance (i.e. the cyber aspect) and energy efficiency (the physical aspect). Driven by the growing needs to green contemporary data centers, this paper uses recent technological advances in data center virtualization and proposes cyber–physical, spatio-temporal (i.e. start time and

In this contribution we introduce the Shape Flow algorithm (SF), a novel method for spatio-temporal 3D pose estimation of a 3D parametric curve. The SF is integrated into a tracking system and its suitability for tracking human body parts in 3D is examined. Based on the example of tracking the human hand-forearm limb it is shown that the use of

A spatio-temporal analysis has been conducted aiming to explore the relationship between traffic congestion and road accidents based on the data on the M25 motorway and its surrounding major roads in England during the period 2003–2007. It was hypothesised that increased traffic congestion may be beneficial to road safety as the number of fatal\\/killed and serious injury (KSI) accidents would

Vector-borne diseases, such as dengue, malaria and chikungunya, are increasing across their traditional ranges and continuing to infiltrate new, previously unaffected, regions. The spatio-temporal evolution of these diseases is determined by the interaction of the host and vector, which is strongly dependent on social structures and mobility patterns. We develop an agent-based model (ABM), in which each individual is explicitly represented and vector populations are linked to precipitation estimates in a tropical setting. The model is implemented on both scale-free and regular networks. The spatio-temporal transmission of chikungunya is analysed and the presence of asymptomatic silent spreaders within the population is investigated in the context of implementing travel restrictions during an outbreak. Preventing the movement of symptomatic individuals is found to be an insufficient mechanism to halt the spread of the disease, which can be readily carried to neighbouring nodes via sub-clinical individuals. Furthermore, the impact of topology structure vs. precipitation levels is assessed and precipitation is found to be the dominant factor driving spatio-temporal transmission. PMID:23958228

A spatio-temporal analysis was undertaken with the aim of identifying the dynamics of herd mean individual cow SCCs (MICSCC) in seasonally calving New Zealand dairy herds. Two datasets were extracted from the Livestock Improvement Corporation's extensive national dairy recording database: (1) milk-recording data aggregated at the herd-level and (2) sales questionnaire data containing information on the size, location and infrastructure of each farm. A Bayesian spatio-temporal modelling approach was applied to the analysis. The data were aggregated by 10 km(2) grid cells and linear regression models were developed with spatially structured and unstructured random effects, a linear temporal trend random effect and spatial-temporal interactions for log-transformed median MISCC (ln(median MISCC)). Significant associations were found between ln(median MISCC) and milk yield, milk fat, milk protein, farm area and number of cups in the dairy. This led us to suggest that SCCs should be adjusted for volume and constituents prior to determining a threshold MISCC for identification of subclinical mastitis (SCM) problem herds. Part, or all, of the temporal trend in MISCC in the spatio-temporal model was accounted for by inclusion of yield and milk constituents as independent variables. This supports the hypothesis of a dilution effect with potential consequences for misdiagnosis of SCM, particularly in late lactation. Unmeasured covariates were similarly likely to be spatially structured and unstructured. PMID:16107283

Smart Antennas with Optical Processing for Broadband BlindSource Separation Paul Smith, Edeline processing, and stock market trend analysis. In this work, we have implemented ICA for a 10-GHz smart antenna, implemented in a free-space prototype integrated on a glass coin the size of a US quarter, Fig.3a. The output

ABSTRACT This paper introduces new algorithms for the blind separation of audio sources using modal decomposition. Indeed, audio signals and, in particular, musical signals can be well approximated,by a sum of damped,sinusoidal (modal) components. Based on this representation, we propose a two steps approach consisting of a signal analysis (extraction of the modal components) followed by a signal synthesis (pairing

The precision of blindsource separation (BSS) by joint approximate decomposition of eigen matrices (JADE) based on fourth-order cumulants is low. In order to overcome this disadvantage, a new algorithm of BSS based on quantum evolutionary algorithm is proposed in this paper. Quantum evolutionary algorithm uses Qubit as basic information-bit for individual code, and finishes the individual evolution with unitary

The scope of this work is the separation of N sources from M linear mixtures when the underlying system is underdetermined, that is, when M¡ N. If the input distribution is sparse the mixing matrix can be estimated either by external optimization or by clustering and, given the mixing matrix, a minimal l1 norm representation of the sources can be

A Neural Architecture for BlindSource Separation Ernesto Tapia and RaÂ´ul Rojas Technical Report B Introduction The blindsource separation (BSS) problem consists on recovering a set of source sig- nals s. The term blind means that the values of the mixing matrix A and the source signals s() are unknown. The BSS

Glacial Isostatic Adjustement (GIA) modeling of solid Earth and gravitational perturbations induced by the Antarctic glaciation across the Eocene/Oligocene transition (EOT; ~34 Ma) predicts a relative sea level (rsl) rise over-ice proximal marine marginal settings. Accordingly, available sedimentary records from the Ross Sea (CIROS1, CRP-3), Prydz Bay (ODP 739, 1166) and Wilkes Land (IOPD U1356, U1360) provide evidence for progressively deeper depositional environments across the late Eocene towards the Oligocene isotope event-1 (Oi-1; 33.7 Ma, which marks a major glacial advancement episode. Since bathymetric changes at these near-field sites are controlled by GIA, the analysis and inter-site comparison of their sedimentary records provide insights into the spatio-temporal evolution of the nascent Antarctic Ice Sheet. In this work we simulate the inception of the Antarctic glaciation by means of a thermomechanical ice sheet-shelf model dynamically coupled to a sea level model based on the gravitationally self-consistent Sea Level Equation (SLE). We generate a set of ice-sheet and rsl scenarios according to (i) different values for the Earth rheological parameters, (ii) initial topographic/bathymetric conditions and (iii) precipitation/temperature patterns. By comparing the observations with the modeling solutions we find that the initial undeformed topography/bathymetry, and consequently its deformations driven by the GIA described by the SLE, are important conditions for a realistic development of the Antarctic ice-sheet.

Axially and temporally resolved optical emission structures were investigated in the rf sheath region of a parallel plate capacitively coupled rf discharge (13.56 MHz) in pure oxygen and tetrafluoromethane. The rf discharge was driven at total pressures of between 10 and 100 Pa, gas flow rate of 3 sccm and rf power in the range 5-100 W. In particular, the emission of the atomic oxygen at 844.6 nm (3p3P ? 3s3S0) and the atomic carbon at 193 nm (3s1P0 ? 2p1D) were imaged with a lens onto the entrance slit of a spectrometer and detected by a fast ICCD-camera. The spatio-temporally resolved analysis of the emission intensity during the rf cycle (73.75 ns) provides two significant excitation processes inside the rf sheath: the electron impact excitation at the sheath edge, and heavy particle impact excitation in front of the powered electrode. In oxygen plasma the emission of atomic oxygen was found in both regions whereas in tetrafluoromethane the emission of atomic carbon was observed only in front of the powered electrode. The experimental results reveal characteristic dependence of the emission pattern in front of the powered electrode on plasma process parameters (self-bias voltage, pressure) and allow an estimation of the excitation threshold energy and effective cross section of energetic heavy particle loss.

Transient Receptor Potential Vanilloid 1 (TRPV1) is a non-selective cation channel that integrates several stimuli into nociception and neurogenic inflammation. Here we investigated the subtle TRPV1 interplay with candidate membrane partners in live cells by a combination of spatio-temporal fluctuation techniques and fluorescence resonance energy transfer (FRET) imaging. We show that TRPV1 is split into three populations with fairly different molecular properties: one binding to caveolin-1 and confined into caveolar structures, one actively guided by microtubules through selective binding, and one which diffuses freely and is not directly implicated in regulating receptor functionality. The emergence of caveolin-1 as a new interactor of TRPV1 evokes caveolar endocytosis as the main desensitization pathway of TRPV1 receptor, while microtubule binding agrees with previous data suggesting the receptor stabilization in functional form by these cytoskeletal components. Our results shed light on the hitherto unknown relationships between spatial organization and TRPV1 function in live-cell membranes. PMID:25764349

The bystander effect in cancer therapy is the inhibition or killing of tumor cells that are adjacent to those directly affected by the agent used for treatment. In the case of chemotherapy, little is known as to how much and by which mechanisms bystander effects contribute to the elimination of tumor cells. This is mainly due to the difficulty to distinguish between targeted and bystander cells since both are exposed to the pharmaceutical compound. We here studied the interaction of tamoxifen-treated human breast cancer MCF-7 cells with their neighboring counterparts by exploiting laminar flow patterning in a microfluidic chip to ensure selective drug delivery. The spatio-temporal evolution of the bystander response in non-targeted cells was analyzed by measuring the mitochondrial membrane potential under conditions of free diffusion. Our data show that the bystander response is detectable as early as 1 hour after drug treatment and reached effective distances of at least 2.8?mm. Furthermore, the bystander effect was merely dependent on diffusible factors rather than cell contact-dependent signaling. Taken together, our study illustrates that this microfluidic approach is a promising tool for screening and optimization of putative chemotherapeutic drugs to maximize the bystander response in cancer therapy. PMID:23750189

A spatially explicit stratification climatology is constructed for the Northwest Atlantic continental shelf using daily averaged hydrographic fields from a 33-year high-resolution, data-assimilated reanalysis dataset. The high-resolution climatology reveals considerable spatio-temporal heterogeneity in seasonal variability with strong interplay between thermal and haline processes. Regional differences in the magnitude and phasing of the seasonal cycle feature earlier development/breakdown in the Middle Atlantic Bight (MAB) and larger peaks on the shelf than in the Gulf of Maine (GoM). The relative contribution of the thermal and haline components to the overall stratification is quantified using a novel diagram composed of two key ratios. The first relates the vertical temperature gradient to the vertical salinity gradient, and the second relates the thermal expansion coefficient to the haline contraction coefficient. Two distinct regimes are identified: the MAB region is thermally-dominated through a larger portion of the year, whereas the Nova Scotian Shelf and the eastern GoM have a tendency towards haline control during the year. The timing of peak stratification and the beginning/end of thermally-positive and thermally-dominant states are examined. Their spatial distributions indicate a prominent latitudinal shift and regionality, having implications for the seasonal cycle of ecosystem dynamics and its interannual variability.

Light carrying an orbital angular momentum (OAM) displays an optical phase front rotating in space and time and a vanishing intensity, a so-called vortex, in the center. Beyond continuous-wave vortex beams, optical pulses with a finite OAM are important for many areas of science and technology, ranging from the selective manipulation and excitation of matter to telecommunications. Generation of vortex pulses with a duration of few optical cycles requires new methods for characterising their coherence properties in space and time. Here we report a novel approach for flexibly shaping and characterising few-cycle vortex pulses of tunable topological charge with two sequentially arranged spatial light modulators. The reconfigurable optical arrangement combines interferometry, wavefront sensing, time-of-flight and nonlinear correlation techniques in a very compact setup, providing complete spatio-temporal coherence maps at minimum pulse distortions. Sub-7?fs pulses carrying different optical angular momenta are generated in single and multichannel geometries and characterised in comparison to zero-order Laguerre-Gaussian beams. To the best of our knowledge, this represents the shortest pulse durations reported for direct vortex shaping and detection with spatial light modulators. This access to space-time coupling effects with sub-femtosecond time resolution opens new prospects for tailored twisted light transients of extremely short duration. PMID:25413789

Stomata, flanked by pairs of guard cells, are small pores on the leaf surfaces of plants and they function to control gas exchange between plants and the atmosphere. Stomata will open when water is available to allow for the uptake of carbon dioxide for photosynthesis. During periods of drought, stomata will close to reduce desiccation stress. As such, optimal functioning of stomata will impact on water use efficiency by plants. The development of an inducible, modular system for robust and targeted gene expression in stomatal guard cells is reported here. It is shown that application of ethanol vapour to activate the gene expression system did not affect the ability of stomata to respond to ABA in bioassays to determine the promotion of stomatal closure and the inhibition of stomatal opening. The system that has been developed allows for robust spatio-temporal control of gene expression in all cells of the stomatal lineage, thereby enabling molecular engineering of stomatal function as well as studies on stomatal development. PMID:19700494

Simultaneous measurements of ultrafine particles (UFPs) were carried out at four sampling locations situated within a 1 km(2) grid area in a Belgian city, Borgerhout (Antwerp). All sampling sites had different orientation and height of buildings and dissimilar levels of anthropogenic activities (mainly traffic volume). The aims were to investigate: (i) the spatio-temporal variation of UFP within the area, (ii) the effect of wind direction with respect to the volume of traffic on UFP levels, and (iii) the spatial representativeness of the official monitoring station situated in the study area. All sampling sites followed similar diurnal patterns of UFP variation, but effects of local traffic emissions were evident. Wind direction also had a profound influence on UFP concentrations at certain sites. The results indicated a clear influence of local weather conditions and the more dominant effect of traffic volumes. Our analysis indicated that the regional air quality monitoring station represented the other sampling sites in the study area reasonably well; temporal patterns were found to be comparable though the absolute average concentrations showed differences of up to 35%. PMID:22705865

Mitochondrial networks in cardiac myocytes under oxidative stress show collective (cluster) behavior through synchronization of their inner membrane potentials (??m). However, it is unclear whether the oscillation frequency and coupling strength between individual mitochondria affect the size of the cluster and vice versa. We used the wavelet transform and developed advanced signal processing tools that allowed us to capture individual mitochondrial ??m oscillations in cardiac myocytes and examine their dynamic spatio-temporal properties. Heterogeneous frequency behavior prompted us to sort mitochondria according to their frequencies. Signal analysis of the mitochondrial network showed an inverse relationship between cluster size and cluster frequency as well as between cluster amplitude and cluster size. High cross-correlation coefficients between neighboring mitochondria clustered longitudinally along the myocyte striations, indicated anisotropic communication between mitochondria. Isochronal mapping of the onset of myocyte-wide ??m depolarization further exemplified heterogeneous ??m among mitochondria. Taken together, the results suggest that frequency and amplitude modulation of clusters of synchronized mitochondria arises by means of strong changes in local coupling between neighboring mitochondria. PMID:20656937

The hyporheic zone in stream ecosystems is a heterogeneous key habitat for species across many taxa. Consequently, it attracts high attention among freshwater scientists, but generally applicable guidelines on sampling strategies are lacking. Thus, the objective of this study was to develop and validate such sampling guidelines. Applying geostatistical analysis, we quantified the spatio-temporal variability of parameters, which characterize the physico-chemical substratum conditions in the hyporheic zone. We investigated eight stream reaches in six small streams that are typical for the majority of temperate areas. Data was collected on two occasions in six stream reaches (development data), and once in two additional reaches, after one year (validation data). In this study, the term spatial variability refers to patch contrast (patch to patch variance) and patch size (spatial extent of a patch). Patch contrast of hyporheic parameters (specific conductance, pH and dissolved oxygen) increased with macrophyte cover (r2?=?0.95, p<0.001), while patch size of hyporheic parameters decreased from 6 to 2 m with increasing sinuosity of the stream course (r2?=?0.91, p<0.001), irrespective of the time of year. Since the spatial variability of hyporheic parameters varied between stream reaches, our results suggest that sampling design should be adapted to suit specific stream reaches. The distance between sampling sites should be inversely related to the sinuosity, while the number of samples should be related to macrophyte cover. PMID:22860053

Transient Receptor Potential Vanilloid 1 (TRPV1) is a non-selective cation channel that integrates several stimuli into nociception and neurogenic inflammation. Here we investigated the subtle TRPV1 interplay with candidate membrane partners in live cells by a combination of spatio-temporal fluctuation techniques and fluorescence resonance energy transfer (FRET) imaging. We show that TRPV1 is split into three populations with fairly different molecular properties: one binding to caveolin-1 and confined into caveolar structures, one actively guided by microtubules through selective binding, and one which diffuses freely and is not directly implicated in regulating receptor functionality. The emergence of caveolin-1 as a new interactor of TRPV1 evokes caveolar endocytosis as the main desensitization pathway of TRPV1 receptor, while microtubule binding agrees with previous data suggesting the receptor stabilization in functional form by these cytoskeletal components. Our results shed light on the hitherto unknown relationships between spatial organization and TRPV1 function in live-cell membranes. PMID:25764349

Disease incidence or mortality data are typically available as rates or counts for specified regions, collected over time. We propose Bayesian nonparametric spatial modeling approaches to analyze such data. We develop a hierarchical specification using spatial random effects modeled with a Dirichlet process prior. The Dirichlet process is centered around a multivariate normal distribution. This latter distribution arises from a log-Gaussian process model that provides a latent incidence rate surface, followed by block averaging to the areal units determined by the regions in the study. With regard to the resulting posterior predictive inference, the modeling approach is shown to be equivalent to an approach based on block averaging of a spatial Dirichlet process to obtain a prior probability model for the finite dimensional distribution of the spatial random effects. We introduce a dynamic formulation for the spatial random effects to extend the model to spatio-temporal settings. Posterior inference is implemented through Gibbs sampling. We illustrate the methodology with simulated data as well as with a data set on lung cancer incidences for all 88 counties in the state of Ohio over an observation period of 21 years. PMID:17926327

We studied the temporal and spatial patterns of leptospirosis, its association with flooding and animal census data in Thailand. Flood data from 2010 to 2012 were extracted from spatial information taken from satellite images. The incidence rate ratio (IRR) was used to determine the relationship between spatio-temporal flooding patterns and the number of human leptospirosis cases. In addition, the area of flood coverage, duration of waterlogging, time lags between flood events, and a number of potential animal reservoirs were considered in a sub-analysis. There was no significant temporal trend of leptospirosis over the study period. Statistical analysis showed an inconsistent relationship between IRR and flooding across years and regions. Spatially, leptospirosis occurred repeatedly and predominantly in northeastern Thailand. Our findings suggest that flooding is less influential in leptospirosis transmission than previously assumed. High incidence of the disease in the northeastern region is explained by the fact that agriculture and animal farming are important economic activities in this area. The periodic rise and fall of reported leptospirosis cases over time might be explained by seasonal exposure from rice farming activities performed during the rainy season when flood events often occur. We conclude that leptospirosis remains an occupational disease in Thailand. PMID:25778527

Bacteria can form complex spatial structures known as biofilms. Biofilm formation is frequently associated with chronic infections due to the greatly enhanced antibiotic resistance of resident bacteria. However, our understanding of the role of basic processes, such as bacteria replication and resource consumption, in controlling the development and temporal change of the spatial structure remains rudimentary. Here, we examine the growth of cultured biofilms by the opportunistic pathogen NTHi. Through spatial information extracted from confocal microscopy images, we quantitatively characterize the biofilm structure as it evolves over time. We find that the equal-time height-height pair correlation function decreases with distance and scales with time for small length scales. Furthermore, both the surface roughness and the correlation length perpendicular to the surface growth direction increase with time initially and then decrease. We construct a spatially resolved agent based model beginning with the simplest possible case of a single bacteria species Fisher-Kolmogorov-Petrovsky-Piscounov equation. We show that it cannot describe the observed spatio-temporal behavior and suggest an improved two-species model that better captures the dynamics of the NTHi system. Supported by The Research Institute at Nationwide Children's Hospital.

White noise techniques have been used widely to investigate sensory systems in both vertebrates and invertebrates. White noise stimuli are powerful in their ability to rapidly generate data that help the experimenter decipher the spatio-temporal dynamics of neural and behavioral responses. One type of white noise stimuli, maximal length shift register sequences (m-sequences), have recently become particularly popular for extracting response kernels in insect motion vision. We here use such m-sequences to extract the impulse responses to figure motion in hoverfly lobula plate tangential cells (LPTCs). Figure motion is behaviorally important and many visually guided animals orient towards salient features in the surround. We show that LPTCs respond robustly to figure motion in the receptive field. The impulse response is scaled down in amplitude when the figure size is reduced, but its time course remains unaltered. However, a low contrast stimulus generates a slower response with a significantly longer time-to-peak and half-width. Impulse responses in females have a slower time-to-peak than males, but are otherwise similar. Finally we show that the shapes of the impulse response to a figure and a widefield stimulus are very similar, suggesting that the figure response could be coded by the same input as the widefield response. PMID:25955416

Snowmelt is a primary water resource for urban/agricultural centers and ecosystems near mountain regions. Stream chemistry from montane catchments is controlled by the flowpaths of water from snowmelt and the timing and duration of snow coverage. A process level understanding of the variability in these processes requires an understanding of the effect of changing climate and anthropogenic loading on spatio-temporal snowmelt patterns. With this as our objective, we applied a snow reconstruction model (SRM) to two well-studied montane watersheds, Tokopah Basin (TOK), California and Green Lake 4 Valley (GLV), Colorado, to examine interannual variability in the timing and location of snowmelt in response to variable climate conditions during the period from 1996 to 2007. The reconstruction model back solves for snowmelt by combining surface energy fluxes, inferred from meteorological data, with sequences of melt season snow images derived from satellite data (i.e., snowmelt depletion curves). The SRM explained 84% of the observed interannual variability in maximum watershed SWE in TOK, with errors ranging from -23 to +27% for the different years. For GLV4, the SRM explained 61% of the interannual variability, with errors ranging from -37 to +34%. In GLV4, interannual variability in snowmelt timing is a factor of four greater than the variability in streamflow timing, unlike in TOK where the ratio is nearly 1:1. We attribute this difference primarily to differences in the magnitude of the turbulent fluxes and the hydrogeology of the two study areas.

The spatio-temporally periodic (STP) potential is interesting in Physics due to the intimate coupling between its time and spatial components. In this paper we begin with a brief discussion of the dynamical behaviors of a single particle in a STP potential and then examine the dynamics of multiple particles interacting in a STP potential via the electric Coulomb potential. For the multiple particle case, we focus on the occurrence of bifurcations when the amplitude of the STP potential varies. It is found that the particle concentration of the system plays an important role; the type of bifurcations that occur and the number of attractors present in the Poincar\\'e sections depend on whether the number of particles in the simulation is even or odd. In addition to the nonlinear dynamical approach we also discuss dependence of the squared fractional deviation of particles kinetic energy of the multiple particle system on the amplitude of the STP potential which can be used to elucidate certain transitions of states; this approach is simple and useful particularly for experimental studies of complicated interacting systems.

Apocarotenoids are a class of compounds that play important roles in nature. In recent years, a prominent role for these compounds in arbuscular mycorrhizal (AM) symbiosis has been shown. They are derived from carotenoids by the action of the carotenoid cleavage dioxygenase (CCD) enzyme family. In the present study, using tomato as a model, the spatio-temporal expression pattern of the CCD genes during AM symbiosis establishment and functioning was investigated. In addition, the levels of the apocarotenoids strigolactones (SLs), C13 ?-ionol and C14 mycorradicin (C13/C14) derivatives were analyzed. The results suggest an increase in SLs promoted by the presence of the AM fungus at the early stages of the interaction, which correlated with an induction of the SL biosynthesis gene SlCCD7. At later stages, induction of SlCCD7 and SlCCD1 expression in arbusculated cells promoted the production of C13/C14 apocarotenoid derivatives. We show here that the biosynthesis of apocarotenoids during AM symbiosis is finely regulated throughout the entire process at the gene expression level, and that CCD7 constitutes a key player in this regulation. Once the symbiosis is established, apocarotenoid flux would be turned towards the production of C13/C14 derivatives, thus reducing SL biosynthesis and maintaining a functional symbiosis. PMID:25480008

Plant environmental responses involve dynamic changes in growth and signaling, yet little is understood as to how progress through these events is regulated. Here, we explored the phenotypic and transcriptional events involved in the acclimation of the Arabidopsis thaliana seedling root to a rapid change in salinity. Using live-imaging analysis, we show that growth is dynamically regulated with a period of quiescence followed by recovery then homeostasis. Through the use of a new high-resolution spatio-temporal transcriptional map, we identify the key hormone signaling pathways that regulate specific transcriptional programs, predict their spatial domain of action, and link the activity of these pathways to the regulation of specific phases of growth. We use tissue-specific approaches to suppress the abscisic acid (ABA) signaling pathway and demonstrate that ABA likely acts in select tissue layers to regulate spatially localized transcriptional programs and promote growth recovery. Finally, we show that salt also regulates many tissue-specific and time point-specific transcriptional responses that are expected to modify water transport, Casparian strip formation, and protein translation. Together, our data reveal a sophisticated assortment of regulatory programs acting together to coordinate spatially patterned biological changes involved in the immediate and long-term response to a stressful shift in environment. PMID:23898029

In this article, an automatic stereoscopic video conversion scheme which accepts MPEG-encoded videos as input is proposed. Our scheme is depth-based, relying on spatio-temporal analysis of the decoded video data to yield depth perception cues, such as temporal motion and spatial contrast, which reflect the relative depths between the foreground and the background areas. Our scheme is shot-adaptive, demanding that shot change detection and shot classification be performed for tuning of algorithm or parameters that are used for depth cue combination. The above-mentioned depth estimation is initially block-based, followed by a locally adaptive joint trilateral upsampling algorithm to reduce the computing load significantly. A recursive temporal filter is used to reduce the possible depth fluctuations (and also artifacts in the synthesized images) resulting from wrong depth estimations. The traditional Depth-Image-Based-Rendering algorithm is used to synthesize the left- and right-view frames for 3D display. Subjective tests show that videos converted by our scheme provide comparable perceived depth and visual quality with those converted from the depth data calculated by stereo vision techniques. Also, our scheme is shown to outperform the well-known TriDef software in terms of human's perceived 3D depth. Based on the implementation by using "OpenMP" parallel programming model, our scheme is capable of executing in real-time on a multi-core CPU platform.

White noise techniques have been used widely to investigate sensory systems in both vertebrates and invertebrates. White noise stimuli are powerful in their ability to rapidly generate data that help the experimenter decipher the spatio-temporal dynamics of neural and behavioral responses. One type of white noise stimuli, maximal length shift register sequences (m-sequences), have recently become particularly popular for extracting response kernels in insect motion vision. We here use such m-sequences to extract the impulse responses to figure motion in hoverfly lobula plate tangential cells (LPTCs). Figure motion is behaviorally important and many visually guided animals orient towards salient features in the surround. We show that LPTCs respond robustly to figure motion in the receptive field. The impulse response is scaled down in amplitude when the figure size is reduced, but its time course remains unaltered. However, a low contrast stimulus generates a slower response with a significantly longer time-to-peak and half-width. Impulse responses in females have a slower time-to-peak than males, but are otherwise similar. Finally we show that the shapes of the impulse response to a figure and a widefield stimulus are very similar, suggesting that the figure response could be coded by the same input as the widefield response. PMID:25955416

GFP-based fluorescence resonance energy transfer (FRET) probes that visualize local activity-changes of Ras and Rho GTPases in living cells are now available for examining the spatio-temporal regulation of these proteins. This article describes principles and strategies to develop intramolecular FRET probes for Ras- and Rho-family GTPases. The procedure for characterizing candidate probes, and image acquisition and processing are also explained. An optimal FRET probe should have (i) a wide dynamic range (which means a high sensitivity), (ii) a high fluorescence intensity, (iii) target specificity, and (iv) a minimal perturbation to endogenous signaling cascades. Although an improvement of FRET probes should be executed in a trial-and-error manner, practical tips for optimization are provided here. In addition, we illustrate some applications of FRET probes for neuronal cells, which are composed of diverse subcellular compartments with different functions; thus, tools to decipher the dynamics of GTPase activity in each compartment have long been desired. PMID:16288890

With the advent of high-throughput measurement techniques, scientists and engineers are starting to grapple with massive data sets and encountering challenges with how to organize, process and extract information into meaningful structures. Multidimensional spatio-temporal biological data sets such as time series gene expression with various perturbations over different cell lines, or neural spike trains across many experimental trials, have the potential to acquire insight about the dynamic behavior of the system. For this potential to be realized, we need a suitable representation to understand the data. A general question is how to organize the observed data into meaningful structures and how to find an appropriate similarity measure. A natural way of viewing these complex high dimensional data sets is to examine and analyze the large-scale features and then to focus on the interesting details. Since the wide range of experiments and unknown complexity of the underlying system contribute to the heterogeneity of biological data, we develop a new method by proposing an extension of Robust Principal Component Analysis (RPCA), which models common variations across multiple experiments as the lowrank component and anomalies across these experiments as the sparse component. We show that the proposed method is able to find distinct subtypes and classify data sets in a robust way without any prior knowledge by separating these common responses and abnormal responses. Thus, the proposed method provides us a new representation of these data sets which has the potential to help users acquire new insight from data. PMID:25901353

The main goal of the project supported in this grant is to contribute to the understanding of localized spatial and spatio-temporal structures far from thermodynamic equilibrium. Here we report on our progress in the study of two classes of systems. (1) We have started to investigate localized wave-pulses in binary-mixture convection. This work is based on our recently derived extension of the conventionally used complex Ginzburg-Landau equations. We are considering three regimes: Dispersion-less supercritical waves; strongly dispersive subcritical waves; and localized waves as bound states of fronts between dispersionless subcritical waves and the motionless conductive state. (2) We have completed our investigation of steady domain structures in which domains of structures with different wave numbers alternate, separated by domain walls. In particular, we have studied their regimes of existence and stability within the framework of a Ginzburg-Landau equation and have compared it to previous results. Those were based on a long-wavelength approximation, which misses certain aspects which turn out to be important for the stability of the domain structures in realistic situations. In addition, we give a description of our work on resonantly forced waves in two-dimensional anisotropic systems.

Drought is one of the most detrimental natural hazards in Yellow River Basin (YRB). In this research, spatio-temporal variation and statistical characteristic of drought in YRB is studied by using dry spell. Two extreme series, including annual maximum series (AMS) and partial duration series (PDS), are used and simulated with generalized extreme value (GEV), generalized Pareto (GP), and Pearson type III (PE3) distributions. The results show that the northern part is drier than the southern part of YRB. Besides, the maximum dry spell usually starts in October, November, and December. According to the trend analysis, mean maximum length of dry spell (MxDS) shows a negative trend in most stations. From the L-moments and Kolmogorov-Smirnov test method, it can be found that GEV model can better fit AMS while GP and PE3 can better fit PDS. Moreover, the quantiles from optimal model of AMS and PDS depict a similar distribution with values increases from south to north. The spatial distribution of scale and location parameters of GEV model for AMS shows a south-to-north gradient, while the distribution of shape parameter is a little irregularity. Furthermore, based on the linear correlation analysis, there is an evident linear relation between location and scale parameters with mean and standard variation of MxDS, respectively.

Soil's potential as a carbon sink for atmospheric CO2 has been widely discussed. Studies of soil organic carbon (SOC) controls, and the subsequent models derived from their findings, have focussed mainly on North American and European regions, and more recently, in regions such as China. In Australia, agricultural practices have led to losses in SOC. This implies that Australian soils have a large potential for increases in SOC. Building on previous work, here we examine the spatial and temporal variation in soil organic carbon (SOC) and its controlling factors controls across a large catchment of approximately 600 km2 in the Upper Hunter Valley, New South Wales, Australia, using data collected from two sampling campaigns, (April 2006 and June-July 2014). Remote sensing using Landsat (30m) and MODIS (250m) NDVI was used to determine if catchment SOC could be predicted using both low and high resolution remote sensing . Relationships between SOC and elevation, aboveground biomass (as represented by NDVI), topographic wetness index (TWI), and incident solar radiation as a surrogate for soil temperature were compared. Initial results demonstrate that higher spatio-temporal resolution may not be necessary for predicting SOC at larger scales. The relationship between SOC and the environmental tracer 137-Cesium as a surrogate for the loss of SOC by erosion also suggests that sediment transport and deposition influences the distribution of SOC. A model developed for the site suggests that simple linear relationships between vegetation, climate and sediment transport could improve SOC predictions.

Despite knowledge that polyploidy is widespread and a major evolutionary force in flowering plant diversification, detailed comparative molecular studies on polyploidy have been confined to only a few species and families. The genus Oryza is composed of 23 species that are classified into ten distinct 'genome types' (six diploid and four polyploid), and is emerging as a powerful new model system to study polyploidy. Here we report the identification, sequence and comprehensive comparative annotation of eight homoeologous genomes from a single orthologous region (Adh1-Adh2) from four allopolyploid species representing each of the known Oryza genome types (BC, CD, HJ and KL). Detailed comparative phylogenomic analyses of these regions within and across species and ploidy levels provided several insights into the spatio-temporal dynamics of genome organization and evolution of this region in 'natural' polyploids of Oryza. The major findings of this study are that: (i) homoeologous genomic regions within the same nucleus experience both independent and parallel evolution, (ii) differential lineage-specific selection pressures do not occur between polyploids and their diploid progenitors, (iii) there have been no dramatic structural changes relative to the diploid ancestors, (iv) a variation in the molecular evolutionary rate exists between the two genomes in the BC complex species even though the BC and CD polyploid species appear to have arisen <2?million years ago, and (v) there are no clear distinctions in the patterns of genome evolution in the diploid versus polyploid species. PMID:20487382

Based on the land use vector data of Wuhan Urban Agglomeration in the years 1990, 2000 and 2009, this paper used Costanza' s evaluation formula to estimate the ecosystem service value (ESV) of the study area according to "equivalent value per unit area of ecosystem services in China" and analyze its spatio-temporal characteristics. Then the correlation analysis was applied to explore the association between the ESV evolution and the land use changes. The results showed that due to the substantial growth of water area, the ESV of Wuhan Urban Agglomeration increased by 9.5% during the study period, which showed an overall rising trend. The ESV of water regulation and waste treatment increased obviously. Furthermore, the ESV changes showed obvious regional differences, which were most significant in Xiantao, Xinzhou and Yunmeng. The ESV was higher in the southeast and lower in the northwest. Over time, a Wuhan-centered "low-high-low" hierarchically distributed structure of ESV was formed in the eastern, western and northern parts. The ecologic dominance of the northern mountainous and hilly region was gradually abated, while a structural expansion with a high-ESV cluster had taken place in the southern part of the region in 2009. During the research period, the temporal change of ESV in Wuhan Urban Agglomeration was positively correlated with the area changes of forestland, water, grassland and cultivated land. However, the spatially balanced distribution of ESV was negatively correlated with the dispersion degrees of the cultivated land and unused land. PMID:24984511

The dynamics of crop genetic diversity need to be assessed to draw up monitoring and conservation priorities. However, few surveys have been conducted in centres of diversity. Sub-Saharan Africa is the centre of origin of sorghum. Most Sahel countries have been faced with major human, environmental and social changes in recent decades, which are suspected to cause genetic erosion. Sorghum is the second staple cereal in Niger, a centre of diversity for this crop. Niger was submitted to recurrent drought period and to major social changes during these last decades. We report here on a spatio-temporal analysis of sorghum genetic diversity, conducted in 71 villages covering the rainfall gradient and range of agro-ecological conditions in Niger's agricultural areas. We used 28 microsatellite markers and applied spatial and genetic clustering methods to investigate change in genetic diversity over a 26-year period (1976-2003). Global genetic differentiation between the two collections was very low (F (st) = 0.0025). Most of the spatial clusters presented no major differentiation, as measured by F (st), and showed stability or an increase in allelic richness, except for two of them located in eastern Niger. The genetic clusters identified by Bayesian analysis did not show a major change between the two collections in the distribution of accessions between them or in their spatial location. These results suggest that farmers' management has globally preserved sorghum genetic diversity in Niger. PMID:20062963

Dengue fever has been recognized as the most important widespread vector-borne infectious disease in recent decades. Over 40% of the world's population is risk from dengue and about 50-100 million people are infected world wide annually. Previous studies have found that dengue fever is highly correlated with climate covariates. Thus, the potential effects of global climate change on dengue fever are crucial to epidemic concern, in particular, the transmission of the disease. This present study investigated the nonlinearity of time-delayed impact of climate on spatio-temporal variations of dengue fever in the southern Taiwan during 1998 to 2011. A distributed lag nonlinear model (DLNM) is used to assess the nonlinear lagged effects of meteorology. The statistically significant meteorological factors are considered, including weekly minimum temperature and maximum 24-hour rainfall. The relative risk and the distribution of dengue fever then predict under various climate change scenarios. The result shows that the relative risk is similar for different scenarios. In addition, the impact of rainfall on the incidence risk is higher than temperature. Moreover, the incidence risk is associated to spatially population distribution. The results can be served as practical reference for environmental regulators for the epidemic prevention under climate change scenarios.

Dengue has been a major public health concern in Australia since it re-emerged in Queensland in 1992-1993. This study explored spatio-temporal distribution and clustering of locally-acquired dengue cases in Queensland State, Australia and identified target areas for effective interventions. A computerised locally-acquired dengue case dataset was collected from Queensland Health for Queensland from 1993 to 2012. Descriptive spatial and temporal analyses were conducted using geographic information system tools and geostatistical techniques. Dengue hot spots were detected using SatScan method. Descriptive spatial analysis showed that a total of 2,398 locally-acquired dengue cases were recorded in central and northern regions of tropical Queensland. A seasonal pattern was observed with most of the cases occurring in autumn. Spatial and temporal variation of dengue cases was observed in the geographic areas affected by dengue over time. Tropical areas are potential high-risk areas for mosquito-borne diseases such as dengue. This study demonstrated that the locally-acquired dengue cases have exhibited a spatial and temporal variation over the past twenty years in tropical Queensland, Australia. There is a clear evidence for the existence of statistically significant clusters of dengue and these clusters varied over time. These findings enabled us to detect and target dengue clusters suggesting that the use of geospatial information can assist the health authority in planning dengue control activities and it would allow for better design and implementation of dengue management programs.

Cell-cell signaling plays a central role in biology, enabling individual cells to coordinate their activities. For example, bacteria show evidence of intercellular signaling through quorum sensing, a regulatory mechanism that launches a coordinated response, depending on the population density. To explore the spatio-temporal development of cell-to-cell signaling, we have created regular, heterotypic microarrays of living cells in hydrogel using time-multiplexed optical traps for submicron positional control of the cell orientation and location without loss of viability. We studied the Lux system for quorum sensing; splitting it into sender and receiver plasmids, which were subsequently introduced into E. Coli. Induced by IPTG, the sender cells express a fluorescent reporter (mRFP1) and the LuxI enzyme that catalyzes the synthesis of a molecular signal AHL that diffuses through the cell membrane and the extra-cellular scaffold. The receiver cells collect the AHL signal that binds to the LuxR regulator and reports it through GFP production. We have measured the time-delay between the onset of mRFP1 and GFP dependence on intercellular spacing in the array.

The analysis of Rayleigh-B{acute e}nard convection in a thin layer of an incompressible fluid caused by heating from below, is based on the Navier-Stokes equations. In planar geometry the Navier-Stokes equations in Bousinesq-approximation reduce to two nonlinear coupled partial differential equations for the velocity flux function {xi} and the temperature deviation {theta}. These equations are analyzed in form of spatial Fourier modes with time-dependent amplitudes. Only modes corresponding to free-free boundary conditions were selected. In this way, a set of ten coupled nonlinear ordinary differential equations for the mode amplitudes was obtained. These equations were solved numerically for different Rayleigh numbers. The temporal information in the ten dimensional phase space of the mode amplitudes is analyzed with respect to the dimension of the attractor. In addition, a time series of flow patterns in real space is constructed. For this spatio-temporal patterns the empirical orthonormal functions are determined and used to find the temporal evolution from the projection onto the basic vectors. Finally the result of different types of analysis were compared. This should lead to a better understanding how to analyze real systems in terms of observational data, e.g., thermal convection on the surface of the sun. {copyright} {ital 1996 American Institute of Physics.}

We study the global spatio-temporal patterns of influenza dynamics. This is achieved by analysing and modelling weekly laboratory confirmed cases of influenza A and B from 138 countries between January 2006 and January 2015. The data were obtained from FluNet, the surveillance network compiled by the the World Health Organization. We report a pattern of skip-and-resurgence behavior between the years 2011 and 2013 for influenza H1N1pdm, the strain responsible for the 2009 pandemic, in Europe and Eastern Asia. In particular, the expected H1N1pdm epidemic outbreak in 2011/12 failed to occur (or "skipped") in many countries across the globe, although an outbreak occurred in the following year. We also report a pattern of well-synchronized wave of H1N1pdm in early 2011 in the Northern Hemisphere countries, and a pattern of replacement of strain H1N1pre by H1N1pdm between the 2009 and 2012 influenza seasons. Using both a statistical and a mechanistic mathematical model, and through fitting the data of 108 countries, we discuss the mechanisms that are likely to generate these events taking into account the role of multi-strain dynamics. A basic understanding of these patterns has important public health implications and scientific significance. PMID:26046930

Understanding the spatio-temporal variability of phytoplankton in aquaculture zones is necessary for the prevention and/or prediction of harmful algal bloom events. Synoptic cruises, time series analyses of physical and biological parameters, and 3D modeling were combined to investigate the variability of phytoplankton biomass in Alfacs Bay at basin scale. This microtidal estuary located in the NW Mediterranean is an important area of shellfish and finfish exploitation, which is regularly affected by toxic outbreaks. Observations showed the existence of a preferential phytoplankton accumulation area on the NE interior of the bay. This pattern can be observed throughout the year, and we show that it is directly linked to the physical forcing in the bay, in particular, the interplay between freshwater input and wind-induced turbulence. Both drivers affect the strength of the estuarine circulation, explaining nearly 75% of the variability in phytoplankton biomass. More cells are retained when stratification is weakened and the estuarine circulation reduced, while flushing rates are higher during times of increased stratification and stronger estuarine flow. This has been confirmed by using a 3D hydrodynamic model with Eulerian tracers. Nutrients, while important to support phytoplankton populations, have been found to play only a secondary role in explaining this variability at basin scale.

As part of an ongoing study on the effects of photoperiodism on the metabolism of steviol glycosides (SVglys) in Stevia rebaudiana, the spatio-temporal variations of free steviol (SV) have now been evaluated. For its quantitation, an internal standard method was used, based upon a specific fluorometric detection of SV as its methoxycoumarinyl derivative. The level of free SV in leaves did not exceed 30 ?g/g dry wt and was at least 1000-fold smaller than that of its glycosidic conjugates. In other organs, free SV was mainly measured in stem tissue and apices, with relatively large amounts measured in the latter. Similarly to SVglys, the content of free SV was influenced by photoperiod and genotype. In plants grown under long-days (LD) of 16 h, more spatial variations were seen compared to those under short-days (SD) of 8h. In the former, upper leaves contained almost four times more free SV compared to lower ones near the end of vegetative growth. In addition, the correlation between SV and its glycosidic conjugates was more linear under SD. Despite the variability of SV levels, a decrease was noted in all conditions after flower opening, which can be related a decreased transcription of the biosynthetic genes involved. PMID:23402803

Spatio-temporal registration of baseline and follow-up intravascular ultrasound (IVUS) pullbacks is of paramount importance in studying the progression/regression of coronary artery disease. Automating these two tasks has the potential to increase productivity when studying large patient populations. Current automated methods are often designed for only one of the two tasks - spatial or temporal. In this paper, we propose an integrated framework which combines the two tasks and employs side-branches to constrain the IVUS pullback registration tasks. For temporal registration, canonical time warping technique optimizes extracted features and weighs cumulative distances. For spatial registration, the search range of cross-correlation based method is constrained by utilizing the angular differences between side-branches. Pilot validation is currently available for ten pairs of IVUS pullback sub-sequences. Results show average spatial and temporal registration errors of 0.49 mm +/- 0.51 mm and 5.56° +/- 3.35°, respectively, a notable improvement over our previous approach (p < 0.001) in temporal registration. Our method has the potential to improve spatial and temporal correspondence in studies of atherosclerotic vascular disease development using IVUS.

In the present study we investigate the rules governing the perception of audiovisual synchrony within spatio-temporally cluttered visual environments. Participants viewed a ring of 19 discs modulating in luminance while hearing an amplitude modulating tone. Each disc modulated with a unique temporal phase (40?ms intervals), with only one synchronized to the tone. Participants searched for the synchronised disc whose spatial location varied randomly across trials. Square-wave modulation facilitated search: the synchronized disc was frequently chosen, with tight response distributions centred near zero-phase lag. In the sinusoidal condition responses were equally distributed over the 19 discs regardless of phase. To investigate whether subjective synchrony in the square-wave condition was limited by spatial or temporal factors we repeated the experiment with either reduced spatial density (9 discs) or temporal density (80?ms phase intervals). Reduced temporal density greatly facilitated synchrony perception but left the synchrony bandwidth unchanged, while no influence of spatial density was found. We conclude that audio-visual synchrony is not strongly constrained by the spatial or temporal density of the visual display, but by a temporal window within which audio-visual events are perceived as synchronous, with a full bandwidth of ~185?ms. PMID:24872325

The use of real-time feedback has expanded fMRI from a brain probe to include potential brain interventions with significant therapeutic promise. However, whereas time-averaged blood oxygenation level-dependent (BOLD) signal measurement is usually sufficient for probing a brain state, the real-time (frame-to-frame) BOLD signal is noisy, compromising feedback accuracy. We have developed a new real-time processing technique (STAR) that combines noise-reduction properties of multi-voxel (e.g., whole-brain) techniques with the regional specificity critical for therapeutics. Nineteen subjects were given real-time feedback in a cognitive control task (imagining repetitive motor activity vs. spatial navigation), and were all able to control a visual feedback cursor based on whole-brain neural activity. The STAR technique was evaluated, retrospectively, for five a priori regions of interest in these data, and was shown to provide significantly better (frame-by-frame) classification accuracy than a regional BOLD technique. In addition to regional feedback signals, the output of the STAR technique includes spatio-temporal activity maps (movies) providing insight into brain dynamics. The STAR approach offers an appealing optimization for real-time fMRI applications requiring an anatomically-localized feedback signal. PMID:21232612

We study the global spatio-temporal patterns of influenza dynamics. This is achieved by analysing and modelling weekly laboratory confirmed cases of influenza A and B from 138 countries between January 2006 and January 2015. The data were obtained from FluNet, the surveillance network compiled by the the World Health Organization. We report a pattern of skip-and-resurgence behavior between the years 2011 and 2013 for influenza H1N1pdm, the strain responsible for the 2009 pandemic, in Europe and Eastern Asia. In particular, the expected H1N1pdm epidemic outbreak in 2011/12 failed to occur (or “skipped”) in many countries across the globe, although an outbreak occurred in the following year. We also report a pattern of well-synchronized wave of H1N1pdm in early 2011 in the Northern Hemisphere countries, and a pattern of replacement of strain H1N1pre by H1N1pdm between the 2009 and 2012 influenza seasons. Using both a statistical and a mechanistic mathematical model, and through fitting the data of 108 countries, we discuss the mechanisms that are likely to generate these events taking into account the role of multi-strain dynamics. A basic understanding of these patterns has important public health implications and scientific significance. PMID:26046930

Jakobshavn Isbræ is the fastest marine-terminating outlet glacier on the Greenland Ice Sheet and has experienced speed up, thinning and increased mass discharge primarily due to ocean-ice interactions at the terminus, over the last two decades. Approximately 60% of the total driving stress within the main ice stream is compensated by resistance due to lateral shear. We have observed the presence of water-filled crevasses, which fill in local depressions and drain seasonally, resulting in meltwater filtration directly into the shear margins. Injection of meltwater into the shear margins can result in shear weakening with implications for observed changes within the ice stream, in addition to, potentially enhancing mass flux into the main trough. Shear weakening, due to infiltrated meltwater, can increase sliding due to basal lubrication or reduce ice stiffness due to cryo-hydrologic warming. In this study, LandSat-7 ETM+ and LandSat-8 OLI images at 15m spatial resolutions are used to characterize the spatio-temporal variability of saturated crevasses during the ablation seasons from 2000 through 2013. Changes in the delineated area of water-filled crevasses are compared to variability in ice surface velocity fields during the analysis period as a first-order assessment on the potential impact these features may have on marginal ice dynamics.

In this study, the spatio-temporal evolution of Yellowstone deformation between 1992 and 2009 is monitored using interferometric synthetic aperture radar (InSAR) data acquired by the European Remote-Sensing Satellites (ERS-1 and ERS-2) and the Environmental Satellite (ENVISAT). These data are combined with continuous global positioning system (GPS) measurements to identify four discrete episodes of caldera subsidence and uplift, these episodes are: 1992-1995 (subsidence of 2.7 cm/year), 1996-2000 (subsidence of 0.5 cm/year, with local uplift of 1.7 cm/year at Norris), 2000-2004 (subsidence of 0.7 cm/year, with local uplift of 0.6 cm/year at Norris), and 2004-2009 (uplift of 3-8 cm/year, with local subsidence of 1-4 cm/year at Norris). We construct the full three-dimensional velocity field of Yellowstone deformation for 2005-2006 from ascending and descending ENVISAT orbits. The InSAR three-dimensional velocity field and three-component GPS measurements indicate that the majority of the observed deformation (3-8 cm/year) across the Yellowstone caldera and near Norris Geyser Basin (NGB) occurred in the vertical direction between the summers of 2005 and 2006. During this time, significant lateral displacements of 3-7 cm/year also occurred in the east-west direction at the southeastern and western rims of the Yellowstone caldera and in the area between Hebgen Lake and NGB. Minor north-south displacements of about 0.2 cm/year also occurred, however, in the southwestern section of the caldera and near Yellowstone Lake during the same period. The calculated three-dimensional velocity field for 2005-2006 implies the existence of two pressure-point sources, beneath the two structural resurgent domes in the Yellowstone caldera, connected by a planar conduit, rather than a single, large sill as proposed in previous studies. Furthermore, no measurable displacements occurred along any fault zone across the caldera during the entire period of observation (1992-2009). Therefore, we infer that magmatic and hydrothermal processes beneath the Yellowstone caldera and NGB were the main sources of deformation.

A blind audio source separation technique with an ill-posed mixing matrix and additive noise is proposed in this work. With this technique, we divide the solution into two steps. The first step is to estimate the ill-posed mixing matrix and the second step is to separate original sources. To estimate the ill-posed mixing matrix, an enhanced soft-assignment method is used

A blind audio source separation technique with an ill-posed mixing matrix and additive noise is proposed in this work. With this technique, we divide the solution into two steps. The first step is to estimate the ill-posed mixing matrix and the second step is to separate orig- inal sources. To estimate the ill-posed mixing matrix, an enhanced soft-assignment method is

This keynote talk describes a state-of-the-art method for the blindsource separation (BSS) of convolutive mixtures of audio\\u000a signals. Independent component analysis (ICA) is used as a major statistical tool for separating the mixtures. We provide\\u000a examples to show how ICA criteria change as the number of audio sources increases. We then discuss a frequency-domain approach\\u000a where simple instantaneous ICA

A measure of temporal predictability is defined and used to separate linear mixtures of signals. Given any set of statistically independent source signals, it is conjectured here that a linear mixture of those signals has the following property: the temporal predictability of any signal mixture is less than (or equal to) that of any of its component source signals. It is shown that this property can be used to recover source signals from a set of linear mixtures of those signals by finding an un-mixing matrix that maximizes a measure of temporal predictability for each recovered signal. This matrix is obtained as the solution to a generalized eigenvalue problem; such problems have scaling characteristics of O(N3), where N is the number of signal mixtures. In contrast to independent component analysis, the temporal predictability method requires minimal assumptions regarding the probability density functions of source signals. It is demonstrated that the method can separate signal mixtures in which each mixture is a linear combination of source signals with supergaussian, subgaussian, and gaussian probability density functions and on mixtures of voices and music. PMID:11440597

Spatio-temporal variations of surface ozone are investigated using the KZ-filter considering meteorological factors based on measurement data at 124 air quality monitoring sites and 72 weather stations over South Korea for the time period of 1999-2010. We use hourly data of ozone (O3), nitrogen dioxide (NO2), temperature (°C), dew-point temperature (°C), sea-level pressure (hPa), wind speed (m/s) and direction (16 cardinal directions), relative humidity (%), and solar insolation (W/m²). Over the Korean peninsula, surface O3 levels at the coastal cities are generally high due to the dynamic effects of the sea breeze and short-lived chlorine species from the sea salt, while those at the Seoul metropolitan area and other inland cities are low due to the NOx titration by anthropogenic emissions. The concentrations of surface O3 have generally increased for the analyzed period with the nationwide average linear trend of +0.26 ppbv/yr (+1.15 %/yr). We also examine the meteorological influences on the surface O3 levels over South Korea using a combined analysis of KZ-filter and multiple linear regressions between surface O3 and meteorological variables. Time-series of surface O3 are decomposed into the short-term, seasonal, and long-term components by the KZ-filter and regressed on meteorological variables. Through probability distribution analysis of the decomposed O3 time-series classified by wind direction, the O3 short-term variation at monitoring sites shows transport effects from the source regions. Impacts of surface temperature on the surface O3 levels are found to be significantly high in the highly populated metropolitan area and inland cities. It implies that those regions will be experiencing more frequent high-ozone events in the future climate conditions with the increase of global temperature. Especially in Seoul, the most populated area in South Korea, the probability of high O3 exceeding air quality standard is almost doubled for the temperature increase of about 4°C. Additional SVD analysis between O3 and NO2 shows similar temporal evolution with spatial patterns of the long-term O3 and NO2 components. This study would provide a reference for appropriate ozone control policy and for the performance evaluation of chemistry climate models over East Asia.

Signal Processing 88 (2008) 1990Â­2007 Blindsource separation for convolutive mixtures based studies the problem of blind separation of convolutively mixed source signals on the basis of the joint. Firstly, a general framework of JD-based blindsource separation (BSS) is reviewed and summarized. Special

ON THE GENERALIZATION OF BLINDSOURCE SEPARATION ALGORITHMS FROM INSTANTANEOUS TO CONVOLUTIVEÂ¨ubeck, 23538 LÂ¨ubeck, Germany ABSTRACT Many convolutive blindsource separation (BSS) ap- proaches are given to illustrate the feasibility of the proposed approach. 1. INTRODUCTION Blindsource separation

BLIND SEPARATION OF MORE SOURCES THAN SENSORS IN CONVOLUTIVE MIXTURES Rasmus Kongsgaard Olsson, Denmark, rko,lkh@imm.dtu.dk ABSTRACT We demonstrate that blind separation of more sources than sensors can. This is an instance of blindsource separation (BSS). Machines capable of em- ulating this function have potential

BLIND SEPARATION OF MORE SOURCES THAN SENSORS IN CONVOLUTIVE MIXTURES Rasmus Kongsgaard Olsson, Denmark, rko,lkh@imm.dtu.dk ABSTRACT We demonstrate that blind separation of more sources than sensors can. This is an instance of blindsource separation (BSS). Machines capable of emÂ­ ulating this function have potential

Fish larvae experience many environmental challenges during development such as variation in water velocity, food availability and predation. The rapid development of structures involved in feeding, respiration and swimming increases the chance of survival. It has been hypothesized that mechanical loading induced by muscle forces plays a role in prioritizing the development of these structures. Mechanical loading by muscle forces has been shown to affect larval and embryonic bone development in vertebrates, but these investigations were limited to the appendicular skeleton. To explore the role of mechanical load during chondrogenesis and osteogenesis of the cranial, axial and appendicular skeleton, we subjected zebrafish larvae to swim-training, which increases physical exercise levels and presumably also mechanical loads, from 5 until 14 days post fertilization. Here we show that an increased swimming activity accelerated growth, chondrogenesis and osteogenesis during larval development in zebrafish. Interestingly, swim-training accelerated both perichondral and intramembranous ossification. Furthermore, swim-training prioritized the formation of cartilage and bone structures in the head and tail region as well as the formation of elements in the anal and dorsal fins. This suggests that an increased swimming activity prioritized the development of structures which play an important role in swimming and thereby increasing the chance of survival in an environment where water velocity increases. Our study is the first to show that already during early zebrafish larval development, skeletal tissue in the cranial, axial and appendicular skeleton is competent to respond to swim-training due to increased water velocities. It demonstrates that changes in water flow conditions can result into significant spatio-temporal changes in skeletogenesis. PMID:22529905

Urbanisation is a ubiquitous phenomenon with greater prominence in developing nations. Urban expansion involves land conversions from vegetated moisture-rich to impervious moisture-deficient land surfaces. The urban land transformations alter biophysical parameters in a mode that promotes development of heat islands and degrades environmental health. This study elaborates relationships among various environmental variables using remote sensing dataset to study spatio-temporal footprint of urbanisation in Surat city. Landsat Thematic Mapper satellite data were used in conjugation with geo-spatial techniques to study urbanisation and correlation among various satellite-derived biophysical parameters, [Normalised Difference Vegetation Index, Normalised Difference Built-up Index, Normalised Difference Water Index, Normalised Difference Bareness Index, Modified NDWI and land surface temperature (LST)]. Land use land cover was prepared using hierarchical decision tree classification with an accuracy of 90.4 % (kappa?=?0.88) for 1990 and 85 % (kappa?=?0.81) for 2009. It was found that the city has expanded over 42.75 km(2) within a decade, and these changes resulted in elevated surface temperatures. For example, transformation from vegetation to built-up has resulted in 5.5?±?2.6 °C increase in land surface temperature, vegetation to fallow 6.7?±?3 °C, fallow to built-up is 3.5?±?2.9 °C and built-up to dense built-up is 5.3?±?2.8 °C. Directional profiling for LST was done to study spatial patterns of LST in and around Surat city. Emergence of two new LST peaks for 2009 was observed in N-S and NE-SW profiles. PMID:22828979

Particulate matter (PM) is closely related to human health, air quality, and climate changes. It has been directly measured on the surface level. However, ground-based measurements have a limitation in spatial coverage of PM concentrations. In order to overcome this spatial limitation of ground measurements, AOD, which is considered as a proxy to PM concentration, was used in this study. AOD was first utilized to figure out the characteristics of PM and was then used to estimate the PM concentrations in Northeast Asia during the DRAGON Northeast-Asia campaign (March-May 2012), using CMAQ-estimated AOD, COMS/GOCI-retrieved AOD, and the AOD data from the DRAGON NE-Asia campaign. First of all, current emission inventories (MEIC and INTEX-B based emission inventories) were evaluated to improve CMAQ modeling results. Next, several algorithms to convert aerosol composition to AOD were evaluated using intensive measurement data from the DRAGON NE-Asia campaign. The accuracy of the CMAQ-estimated AOD was further evaluated with hourly observing GOCI-retrieved AOD. After the evaluation, CMAQ-calculated AOD was mathematically combined with GOCI-retrieved AOD via data assimilation. After this, AERONET AOD measured by the DRAGON NE-Asia campaign was again combined with the assimilated AOD from CMAQ and GOCI AODs to produce more accurate spatio-temporal AOD fields over Northeast Asia. Using several relationships between PM (PM10 and PM2.5) and AOD, the best surface-PM concentrations over the entire domain were calculated. It was then evaluated with ground-based PM2.5 measurements from the DRAGON NE-Asia campaign. A good agreement between estimated PM2.5 and measured PM2.5 over the domain was found. Finally, the PM and AOD information was used to investigate the effects of transboundary PM pollution from China to the Korean peninsula.

In recent years, dengue has become a major international public health concern. In Thailand it is also an important concern as several dengue outbreaks were reported in last decade. This paper presents a GIS approach to analyze the spatial and temporal dynamics of dengue epidemics. The major objective of this study was to examine spatial diffusion patterns and hotspot identification for reported dengue cases. Geospatial diffusion pattern of the 2007 dengue outbreak was investigated. Map of daily cases was generated for the 153 days of the outbreak. Epidemiological data from Chachoengsao province, Thailand (reported dengue cases for the years 1999-2007) was used for this study. To analyze the dynamic space-time pattern of dengue outbreaks, all cases were positioned in space at a village level. After a general statistical analysis (by gender and age group), data was subsequently analyzed for temporal patterns and correlation with climatic data (especially rainfall), spatial patterns and cluster analysis, and spatio-temporal patterns of hotspots during epidemics. The results revealed spatial diffusion patterns during the years 1999-2007 representing spatially clustered patterns with significant differences by village. Villages on the urban fringe reported higher incidences. The space and time of the cases showed outbreak movement and spread patterns that could be related to entomologic and epidemiologic factors. The hotspots showed the spatial trend of dengue diffusion. This study presents useful information related to the dengue outbreak patterns in space and time and may help public health departments to plan strategies to control the spread of disease. The methodology is general for space-time analysis and can be applied for other infectious diseases as well. PMID:21318014

Brain changes due to development and maturation, normal aging, or degenerative disease are continuous, gradual, and variable across individuals. To quantify the individual progression of brain changes, we propose a spatio-temporal methodology based on Hidden Markov Models (HMM), and apply it on four-dimensional structural brain magnetic resonance imaging series of older individuals. First, regional brain features are extracted in order to reduce image dimensionality. This process is guided by the objective of the study or the specific imaging patterns whose progression is of interest, for example, the evaluation of Alzheimer-like patterns of brain change in normal individuals. These regional features are used in conjunction with HMMs, which aim to measure the dynamic association between brain structure changes and progressive stages of disease over time. A bagging framework is used to obtain models with good generalization capability, since in practice the number of serial scans is limited. An application of the proposed methodology was to detect individuals with the risk of developing MCI, and therefore it was tested on modeling the progression of brain atrophy patterns in older adults. With HMM models, the state-transition paths corresponding to longitudinal brain changes were constructed from two completely independent datasets, the Alzheimer Disease Neuroimaging Initiative and the Baltimore Longitudinal Study of Aging. The statistical analysis of HMM-state paths among the normal, progressive MCI, and MCI groups indicates that, HMM-state index 1 is likely to be a predictor of the conversion from cognitively normal to MCI, potentially many years before clinical symptoms become measurable. PMID:24706564

Understanding the spatial and temporal variation of drought is essentially important in drought assessment. In most previous studies, drought event is usually identified in space and time separately, ignoring the nature of the dynamic processes. In order to better understand how drought changes have taken place in China during the past half-century, we carried out a comprehensive analysis of their spatio-temporal variation based on multiple drought indices from a climatic perspective. A 3-dimensional clustering method is developed to identify drought events in China from 1961 to 2012 based on the 0.25° gridded indices of SPI3 (3 months Standardized Precipitation Index), RDI3 (3 months Reconnaissance Drought Index) and SPEI3 (3 months Standardized Precipitation Evapotranspiration Index). Drought events are further characterized by five parameters: duration, affected area, severity, intensity, and centroid. Remotely sensed soil moisture data were used to validate the rationality of identified drought events. The results show that the two most severe drought events in the past half century which occurred in the periods 1962-1963 and 2010-2011 swept more than half of the non-arid regions in China. Large magnitude droughts were usually centered in the region from North China Plain to the downstream of Yangtze River. The western part of North China Plain, Loess Plateau, Sichuan Basin and Yunnan-Guizhou Plateau had a significant drying trend, which is mainly caused by the significant decrease of precipitation. The three drought indices have almost the same performance in the humid regions, while SPI and RDI were found to be more appropriate than SPEI in the arid regions.

Spicules have been observed on the Sun for more than a century, typically in chromospheric lines such as H{alpha} and Ca II H. Recent work has shown that so-called 'type II' spicules may have a role in providing mass to the corona and the solar wind. In chromospheric filtergrams these spicules are not seen to fall back down, and they are shorter lived and more dynamic than the spicules that have been classically reported in ground-based observations. Observations of type II spicules with Hinode show fundamentally different properties from what was previously measured. In earlier work we showed that these dynamic type II spicules are the most common type, a view that was not properly identified by early observations. The aim of this work is to investigate the effects of spatio-temporal resolution in the classical spicule measurements. Making use of Hinode data degraded to match the observing conditions of older ground-based studies, we measure the properties of spicules with a semi-automated algorithm. These results are then compared to measurements using the original Hinode data. We find that degrading the data has a significant effect on the measured properties of spicules. Most importantly, the results from the degraded data agree well with older studies (e.g., mean spicule duration more than 5 minutes, and upward apparent velocities of about 25 km s{sup -1}). These results illustrate how the combination of spicule superposition, low spatial resolution and cadence affect the measured properties of spicules, and that previous measurements can be misleading.

In recent years, dengue has become a major international public health concern. In Thailand it is also an important concern as several dengue outbreaks were reported in last decade. This paper presents a GIS approach to analyze the spatial and temporal dynamics of dengue epidemics. The major objective of this study was to examine spatial diffusion patterns and hotspot identification for reported dengue cases. Geospatial diffusion pattern of the 2007 dengue outbreak was investigated. Map of daily cases was generated for the 153 days of the outbreak. Epidemiological data from Chachoengsao province, Thailand (reported dengue cases for the years 1999–2007) was used for this study. To analyze the dynamic space-time pattern of dengue outbreaks, all cases were positioned in space at a village level. After a general statistical analysis (by gender and age group), data was subsequently analyzed for temporal patterns and correlation with climatic data (especially rainfall), spatial patterns and cluster analysis, and spatio-temporal patterns of hotspots during epidemics. The results revealed spatial diffusion patterns during the years 1999–2007 representing spatially clustered patterns with significant differences by village. Villages on the urban fringe reported higher incidences. The space and time of the cases showed outbreak movement and spread patterns that could be related to entomologic and epidemiologic factors. The hotspots showed the spatial trend of dengue diffusion. This study presents useful information related to the dengue outbreak patterns in space and time and may help public health departments to plan strategies to control the spread of disease. The methodology is general for space-time analysis and can be applied for other infectious diseases as well. PMID:21318014

A child's natural gait pattern may be affected by the gait laboratory environment. Wearable devices using body-worn sensors have been developed for gait analysis. The purpose of this study was to validate and explore the use of foot-worn inertial sensors for the measurement of selected spatio-temporal parameters, based on the 3D foot trajectory, in independently walking children with cerebral palsy (CP). We performed a case control study with 14 children with CP aged 6-15 years old and 15 age-matched controls. Accuracy and precision of the foot-worn device were measured using an optical motion capture system as the reference system. Mean accuracy ± precision for both groups was 3.4 ± 4.6 cm for stride length, 4.3 ± 4.2 cm/s for speed and 0.5 ± 2.9° for strike angle. Longer stance and shorter swing phases with an increase in double support were observed in children with CP (p=0.001). Stride length, speed and peak angular velocity during swing were decreased in paretic limbs, with significant differences in strike and lift-off angles. Children with cerebral palsy showed significantly higher inter-stride variability (measured by their coefficient of variation) for speed, stride length, swing and stance. During turning trajectories speed and stride length decreased significantly (p<0.01) for both groups, whereas stance increased significantly (p<0.01) in CP children only. Foot-worn inertial sensors allowed us to analyze gait spatiotemporal data outside a laboratory environment with good accuracy and precision and congruent results with what is known of gait variations during linear walking in children with CP. PMID:24044970

In general, there have been various methods of estimating groundwater recharge such as baseflow separation approaches, water budget analyses based on lumped conceptual models, and the water table fluctuation method (WTF) by using data from groundwater monitoring wells. However, groundwater recharge rates show spatial-temporal variability due to climatic conditions, land use, and hydrogeological heterogeneity, so these methods have various limitations in dealing with these characteristics. To overcome these limitations, we present a novel application of estimating recharge based on water balance components from the combined SWAT-MODFLOW model, which is an integrated surface-ground water model. During the process of computing recharge, the time delay is very important factor. SWAT model uses single linear reservoir storage module with an exponential decay weighting function for accounting time delay through vadose zone. However, single reservoir module has limitation on the long delay time. So we suggest a multi-reservoir storage routing module instead of single one, which represents a more realistic time delay through the vadose zone. By using this module, the parameter related to the delay time could be optimized by checking the correlation between simulated recharge and observed groundwater levels. The final step of this procedure is to compare simulated groundwater levels as well as simulated watershed runoff with observed ones. This method is applied to several watersheds in Korea for the purpose of testing the procedure for proper estimation of spatio-temporal groundwater recharge distribution. As this application procedure of estimating recharge has the advantages of the effectiveness of a watershed model as well as the accuracy of the WTF method, the estimated daily recharge rate could be thought as an improved estimate reflecting the heterogeneity of hydrogeology, climatic conditions, land use, as well as the physical behavior of water in soil layers and aquifers.

This paper deals with the application of the multi-resolution technique based on spherical wavelet theory to determine regional spatio-temporal gravity models using spaceborne gravimetry data (GRACE, GOCE). The basic idea of the multi-resolution representation is to split an input gravity signal into a number of detail signals. Since each detail signal (levels) is related to a special frequency-band it is in fact computable from different data sets covering specific parts of the frequency spectrum. In our approach we estimate at first the long-wavelength (low-level) part of the gravity field using in situ potential measurements derived from GRACE inter-satellite range-rate and accelerometer measurements using the energy conservation approach. This way we obtain both wavelet based regional and temporal gravity fields with spatial resolution up to ~300 km and varying sampling (each detail signal is computed from a GRACE data frame of different length; between several days for the long wavelengths to a month for the more detailed structures) as well as a mean gravity field model based on more than two years of GRACE data. Solution constraints, when necessary, are implemented in a data-driven optimal procedure. For the static case we compute in addition the high-level detail signals or short-wavelength gravity components, where available, from Faye anomalies derived from terrestrial, airborne and altimetric observations. Since the medium-level detail signals are present in both the satellite and in the surface data they require the use of a combination method. As an example, we apply the developed procedures to determine a regional time-dependent gravity model of the Amazon basin, a mean high-resolution gravity model for Colombia, and other selected regions. It is anticipated that the developed methodologies can be applied to other regions of interest and have the distinct capability of spatial and temporal signal enhancement using the multi-resolution representation of the gravity field model.

We investigate spatio-temporal properties of earthquake patterns in the San Jacinto fault zone (SJFZ), California, between Cajon Pass and the Superstition Hill Fault, using long records of simulated seismicity constrained by available data. The model provides an effective realization (e.g. Ben-Zion 1996; Zöller et al. 2007) of a large segmented strike-slip fault zone in 3D elastic half space, with heterogeneous distributions of static/kinetic friction and creep properties, and boundary conditions consisting of constant velocity motion around the fault. The computational section of the fault contains small brittle slip patches which fail during earthquakes and may undergo some creep deformation between events. The creep rates increase to the end points of the computational section and with depth. Two significant offsets of the SJFZ at San Jacinto Valley and Coyote Ridge are modeled by strength heterogeneities. The simulated catalogs are compared to the seismicity recorded at the SJFZ since 1932 and to recently reported results on paleoearthquakes at sites along the SJFZ at Hog Lake (HL) and Mystic Lake (ML) in the last 1500 years (e.g. Onderdonk et al., 2012; Rockwell et al., 2012). We address several questions including the following intriguing issue raised by the available paleoseismological data: are large earthquakes with signatures in ML and HL typically correlated? In particular: is a typical paleoevent in HL an incomplete rupture that is continued later in ML, and vice versa? The simulation results provide insights on the statistical significance of these and other patterns, and the ability of the SJFZ to produce large earthquakes which have not been observed in recent decades.

Debris flows are a major threat in many parts of the Alps, where they repeatedly cause severe damage to infrastructure and transportation corridors or even loss of life. Nonetheless, the spatial behavior of past debris-flow activity and the analysis of areas affected during particular events have been widely neglected in reconstructions so far. It was therefore the purpose of this study to reconstruct spatio-temporal patterns of past debris flows on a forested cone in the Swiss Alps (Bruchji torrent, Blatten, Valais). The analysis of past events was based on a detailed geomorphic map (1:1000) of all forms related to debris flows as well as on tree-ring series from 401 heavily affected trees ( Larix decidua Mill. and Picea abies (L.) Karst.) growing in or next to deposits. The samples were analyzed and growth disturbances related to debris-flow activity assessed, such as tangential rows of traumatic resin ducts, the onset of reaction wood or abrupt growth suppression or release. In total, 960 growth disturbances were identified in the samples, belonging to 40 different event years between A.D. 1867 and 2005. In addition, the coupling of tree-ring data with the geomorphic map allowed reconstruction of eleven formerly active channels and spatial representation of individual events. Based on our results we believe that before 1935, debris flows preferentially used those channels located in the western part of the cone, whereas the eastern part of the cone remained widely unaffected. The spatial representation of the 40 events also allowed identification of five different spatial patterns for debris flows at the study site.

Phenological observations of flowering date, budding date or senescence provide very valuable time series. They hold out the prospect for relating plant growth to environmental and climatic factors and hence for engendering a better understanding of plant physiology under natural conditions. The statistical establishment of associations between time series of phenological data and climatic factors provides a means of aiding forecasts of the biological impacts of future climatic change. However, it must be kept in mind that plant growth and behaviour vary spatially as well as temporally. Environmental, climatic and genetic diversity can give rise to spatially structured variation on a range of scales. The variations extend from large-scale geographical (clinal) trends, through medium-scale population and sub-population fluctuations, to micro-scale differentiation among neighbouring plants, where spatially close individuals are found to be genetically more alike than those some distance apart. We developed spatio-temporal phenological models that allow observations from multiple locations to be analysed simultaneously. We applied the models to the first-flowering dates of Prunus padus and Tilia cordata from localities as far apart as Norway and the Caucasus. Our growing-degree-day approach yielded a good fit to the available phenological data and yet involved only a small number of model parameters. It indicated that plants should display different sensitivities to temperature change according to their geographical location and the time of year at which they flower. For spring-flowering plants, we found strong temperature sensitivities for islands and archipelagos with oceanic climates, and low sensitivities in the interiors of continents.

Nitrate (NO) is a major contaminant and threat to groundwater quality in Texas. High-NO groundwater used for irrigation and domestic purposes has serious environmental and health implications. The objective of this study was to evaluate spatio-temporal trends in groundwater NO concentrations in Texas on a county basis from 1960 to 2010 with special emphasis on the Texas Rolling Plains (TRP) using the Texas Water Development Board's groundwater quality database. Results indicated that groundwater NO concentrations have significantly increased in several counties since the 1960s. In 25 counties, >30% of the observations exceeded the maximum contamination level (MCL) for NO (44 mg L NO) in the 2000s as compared with eight counties in the 1960s. In Haskell and Knox Counties of the TRP, all observations exceeded the NO MCL in the 2000s. A distinct spatial clustering of high-NO counties has become increasingly apparent with time in the TRP, as indicated by different spatial indices. County median NO concentrations in the TRP region were positively correlated with county-based area estimates of crop lands, fertilized croplands, and irrigated croplands, suggesting a negative impact of agricultural practices on groundwater NO concentrations. The highly transmissive geologic and soil media in the TRP have likely facilitated NO movement and groundwater contamination in this region. A major hindrance in evaluating groundwater NO concentrations was the lack of adequate recent observations. Overall, the results indicated a substantial deterioration of groundwater quality by NO across the state due to agricultural activities, emphasizing the need for a more frequent and spatially intensive groundwater sampling. PMID:23128738

Reciprocal connections, in essence, are the dynamic wiring (connections) of the neural network circuitry. Given the high complexity of the neural circuitry in the human brain, it is quite a challenge to study the dynamic wiring of highly parallel and widely distributed neural networks. The measurements of stimulus evoked coherent oscillations provide indirect evidence of dynamic wiring. In this study, in addition to the coherent oscillation measurements, two more techniques are discussed for testing possible dynamic wiring: measurements of spatio-temporal interactions beyond the classical receptive fields, and neural structural testing using nonlinear systems analysis.

In this paper, we explored the application of computer vision technology to automatically detect the tasseling stage of maize. The commonly used HOG/SVM detection framework is chosen to recognize the ears of maize for determining the occurrence time of the stage. However, it cannot guarantee high precision rate. Thus, we proposed a new method called Spatio-temporal Saliency Mapping to highlight the ear while suppress the background, which significantly improve the detection performance. Comparing experiment has been carried out to testify the validity of our method and the results indicate that our method can meet the demand for practical observation.

Channel equalization and identification appear as key issues in improving wireless communications. It is known that the linearization of the GMSK modulation leads to a continuous phase OQPSK which can be considered as a Minimum SHift Keying (MSK) modulation. Thus methods of equalization and identification when the channel is input by MSK modulated signals is worth to look at. Most algorithms consider MSK signals as two independent white binary PAM staggered signals; this is not the case in our approach. Here, MSK signals are seen, after sampling at baud rate, as colored complex discrete signals. Even if this view of MSK modulation is quite simple, it has never been utilized with purpose of blind equalization. The particular statistics of such signals are studied, yielding an original closed-form analytical solution to blind equalization, both in the monovariate case and in the source separation problem. Simulations show a good behavior of the algorithms in terms of bit error rate as a function of SNR, both in the case of blind equalization and source separation.

Multispectral optoacoustic (photoacoustic) tomography (MSOT) is a hybrid modality that can image through several millimeters to centimeters of diffuse tissues, attaining resolutions typical of ultrasound imaging. The method can further identify tissue biomarkers by decomposing the spectral contributions of different photo-absorbing molecules of interest. In this work we investigate the performance of blindsource unmixing methods and spectral fitting approaches in decomposing the contributions of fluorescent dyes from the tissue background, based on MSOT measurements in mice. We find blind unmixing as a promising method for accurate MSOT decomposition, suitable also for spectral unmixing in fluorescence imaging. We further demonstrate its capacity with temporal unmixing on real-time MSOT data obtained in-vivo for enhancing the visualization of absorber agent flow in the mouse vascular system. PMID:21369139

In order to investigate spatio-temporal variations in the composition and origin of the benthic organic matter (OM) at the sediment surface in mangrove receiving shrimp farm effluents, fatty acid (FA) biomarkers, natural stable isotopes (?(13)C and ?(15)N), C:N ratios and chlorophyll-a (chl-a) concentrations were determined during the active and the non-active period of the farm. Fatty acid compositions in surface sediments within the mangrove forest indicated that organic matter inputs varied along the year as a result of farm activity. Effluents were the source of fresh particulate organic matter for the mangrove, as evidenced by the unsaturated fatty acid (UFA) distribution. The anthropogenic MUFA 18:1?9 was not only accumulated at the sediment surface in some parts of the mangrove, but was also exported to the seafront. Direct release of bacteria and enhanced in situ production of fungi, as revealed by specific FAs, stimulated mangrove litter decomposition under effluent runoff condition. Also, microalgae released from ponds contributed to maintain high benthic chl-a concentrations in mangrove sediments in winter and to a shift in microphytobenthic community assemblage. Primary production was high whether the farm released effluent or not which questioned the temporary effect of shrimp farm effluent on benthic microalgae dynamic. This study outlined that mangrove benthic organic matter was qualitatively and quantitatively affected by shrimp farm effluent release and that responses to environmental condition changes likely depended on mangrove stand characteristics. PMID:25634734

Damaging thermal stimuli trigger long-lasting variation potentials (VPs) in higher plants. Owing to limitations in conventional plant electrophysiological recording techniques, recorded signals are composed of signals originating from all of the cells that are connected to an electrode. This limitation does not enable detailed spatio-temporal distributions of transmission and electrical activities in plants to be visualised. Multi-electrode array (MEA) enables the recording and imaging of dynamic spatio-temporal electrical activities in higher plants. Here, we used an 8 × 8 MEA with a polar distance of 450??m to measure electrical activities from numerous cells simultaneously. The mapping of the data that were recorded from the MEA revealed the transfer mode of the thermally induced VPs in the leaves of Helianthus annuus L. seedlings in situ. These results suggest that MEA can enable recordings with high spatio-temporal resolution that facilitate the determination of the bioelectrical response mode of higher plants under stress. PMID:24961469

Background Buruli ulcer (BU) is an extensively damaging skin infection caused by Mycobacterium ulcerans, whose transmission mode is still unknown. The focal distribution of BU and the absence of interpersonal transmission suggest a major role of environmental factors, which remain unidentified. This study provides the first description of the spatio-temporal variations of BU in an endemic African region, in Akonolinga, Cameroon. We quantify landscape-associated risk of BU, and reveal local patterns of endemicity. Methodology/Principal Findings From January 2002 to May 2012, 787 new BU cases were recorded in 154 villages of the district of Akonolinga. Incidence per village ranged from 0 (n?=?59 villages) to 10.4 cases/1000 person.years (py); median incidence was 0.4 cases/1,000py. Villages neighbouring the Nyong River flood plain near Akonolinga town were identified as the highest risk zone using the SPODT algorithm. We found a decreasing risk with increasing distance to the Nyong and identified 4 time phases with changes in spatial distribution. We classified the villages into 8 groups according to landscape characteristics using principal component analysis and hierarchical clustering. We estimated the incidence ratio (IR) associated with each landscape using a generalised linear model. BU risk was highest in landscapes with abundant wetlands, especially cultivated ones (IR?=?15.7, 95% confidence interval [95%CI]?=?15.7[4.2–59.2]), and lowest in reference landscape where primary and secondary forest cover was abundant. In intermediate-risk landscapes, risk decreased with agriculture pressure (from IR[95%CI]?=?7.9[2.2–28.8] to 2.0[0.6–6.6]). We identified landscapes where endemicity was stable and landscapes where incidence increased with time. Conclusion/Significance Our study on the largest series of BU cases recorded in a single endemic region illustrates the local evolution of BU and identifies the Nyong River as the major driver of BU incidence. Local differences along the river are explained by wetland abundance and human modification of the environment. PMID:25188464

Numerous studies have shown that the changes in land cover/use affect significantly the hydrological regime, which in turn influence the surface water quality. It is known that, at the catchment scale, hydrological modelling is a favourable tool for discharge and nutrients transport (such as Nitrogen and Phosphorus) predictions. The semi-distributed hydrological water quality HYPE (Hydrological Predictions for the Environment) model, has been evaluated for different catchments, and has been shown to reliably reproduce the measured data. The aim of this study was to test the spatio-temporal transferability of the HYPE model in Central Germany. First, the spatial transferability of the HYPE model was tested using two mesoscale catchments with different physiographical characteristics. To achieve our gaols, the Selke (463 km²) and Weida (99.5 km²) catchments, which are two small tributaries of the Elbe river basin were utilized. Second, the temporal transferability of the HYPE model was tested in the Weida catchment using different periods, where different patterns of nitrogen leaching were measured due to two considerable shifts in land use intensities and fertilizers application rates in 1990 and 1997. For Selke, the HYPE model reproduced reasonably well the discharge and IN monthly loads (with lowest NSE of 0.86 and 0.69 for discharge and IN loads, respectively). Also, results showed that only a NSE of 0.30 was obtained for the Weida catchment, in situations where the same best-optimized values from Selke was utilized, reflecting the controlling factors of land use and topography on the runoff generation. However, when the physiographical characteristics of the Weida catchment were considered during the calibration and validation phases (1997-2000 and 2001-2004, respectively, daily data), the HYPE model could reasonably predict the measured discharge and IN concentrations with similar performance as the Selke. In addition, the temporal transferability of the HYPE model was tested successfully in the Weida catchment by representing the dynamics of IN concentrations during the periods of 1983-2004 by adjusting land use intensities and fertilizers inputs in three different periods, respectively. The preliminary results of this study will be discussed and presented.

The spatio-temporal variability of boreal summer monsoon onset over the Philippines is studied through the analysis of daily rainfall data across a network of 76 gauges for the period 1977 to 2004 and the pentad Merged Analysis of Precipitation from the US Climate Prediction Center from 1979 to 2006. The onset date is defined using a local agronomic definition, namely the first wet day of a 5-day period receiving at least 40 mm without any 15-day dry spell receiving <5 mm in the 30 days following the start of that period. The onset is found to occur rather abruptly across the western Philippines around mid-May on average and is associated with the set-up of a “classical” monsoonal circulation with low-level easterlies subsequently veering to southerly, and then southwesterly. The onset manifests itself merely as a seasonal increase of rainfall over the eastern Philippines, where rainfall occurs throughout most of the year. Interannual variability of the onset date is shown to consist of a spatially coherent large-scale component, rather similar over the western and eastern Philippines, with a moderate to high amount of local-scale (i.e. station scale) noise. In consequence, the large-scale signal can be easily retrieved from any sample of at least 5-6 stations across the network although the local-scale coherence and fingerprint of the large-scale signal of the onset date are found to be stronger over the central Philippines, roughly from Southern Luzon to Northern Mindanao. The seasonal predictability of local onset is analyzed through a cross-validated canonical correlation analysis using tropical Pacific and Indian Ocean sea surface temperature in March and the 850 hPa May wind field from dynamical forecast models as predictors. The regional-scale onset, defined as the average of standardized local-scale anomalies in onset date, shows good predictive skill ( r ? 0.8). Moreover, most of the stations show weak to moderate skill (median skill = 0.28-0.43 depending on the scheme) with spatial averaging across stations typically increasing skill to >0.6.

Previous studies of spatio-temporal evolution of slip on a fault governed by rate-and-state friction (e.g., Rice, 1993; Ben-Zion and Rice, 1995, 1997; Tullis, 1996; Lapusta et al., 2000) employed frictional properties corresponding to fairly homogeneous faults. In most cases, the only types of heterogeneities were the lab-based depth-variations of the parameters a and b that produce transitions between stable velocity-strengthening and unstable velocity-weakening regimes. In this study we use a constant a-b profile and a depth-dependent distribution of the critical slip distance parameter L. In addition, correlated heterogeneities of L along strike are used to model geometrical heterogeneities on faults related to roughness. More specifically, we will perform 3D quasi-static and quasi-dynamic simulations of slip on a strike-slip fault using a family of 2D anisotropic correlated distributions of L having different correlation lengths along strike and downdip. The depth-variation of L over the depth range 3km < z < 12 km, representing the seismogenic zone, accounts for an overall reduction of the gouge thickness (and hence L) with depth. Above and below the seismogenic zone, L increases rapidly. The variations of L along strike are chosen to provide approximate representations of faults at different evolutionary stages. Relatively smooth mature faults (like the San Andreas) will be represented with distributions that have large horizontal correlation length, while distributions with small correlation lengths are used to represent rougher immature faults (like the San Jacinto and faults in the eastern CA shear zone). The choices of representative correlation lengths is guided and constrained by maps of fault structures of the type compiled by Wesnousky (1994), and by the compilation of inverted slip histories. The 3D code with various cases of anisotropic correlated distributions of L will be used to study many issues related to observed complex behavior of seismogenic faults including: (1) Nucleation and arrest properties of failure episodes on a heterogeneous fault governed by RSD friction. (2) Comparison between properties of final simulated slip histories and those of the inverted slip histories. (3) Frequency-size and temporal statistics of simulated earthquakes on a heterogeneous fault governed by rate-and-state friction.

Himalayan mountain system in the Indian sub-continent are among the most ecologically sensitive environments and are also a repository of biodiversity and ecosystem services. Over the last few decades, land transformation related to exploitative land uses is among the main drivers of changing vegetation cover and productivity in western Himalayas. In a region where field based research is challenging due to heterogeneous relief and high altitude, quantifying changes in vegetation productivity using remote sensing can provide essential information regarding trends in vegetation cover and its linkages with Land Use Land Cover (LULC) dynamics. We conducted seasonal trend analysis (STA) on MODIS NDVI time-series data (2000-2014) over Uttarakhand Himalayas and examined spatio-temporal patterns in vegetation trend and its association with altitudinal gradient and LULC dynamics. In STA the first step determines the annual mean and seasonal NDVI patterns and the second step analyzes the non-parametric trend in magnitude and timing of the annual mean and seasonal NDVI cycle. In total 3286.82 km2 (6.9% vegetated area of Uttarakhand) showed significant trend (p<0.01) in mean annual greenness. While areas <800 m elevation showed dominant negative trend in mean annual greenness, area between 800-1600m showed mostly positive trend and majority of areas >1600 m were characterized by negative trend in mean annual greenness. Considering LULC characteristics, majority of intensively cultivated irrigated croplands in the Himalayan foothills as well as areas around growing urban centers showed widespread negative trend in mean annual greenness, which was contrastingly different from rainfed cultivation areas that showed dominant positive trend. Negative trend in mean annual greenness was observed to be consistent with increasing altitude and particularly in closed needle leaf forests and alpine shrublands except areas where human impacts has led to mixed patterns. Trends in the annual seasonal timing of NDVI indicated an earlier green-up for most parts of the Uttarakhand Himalayas. These results are somewhat surprising and are partially in agreement with previous studies that suggest increasing brownness in Himalayan vegetation based on analyses of comparatively much coarser spatial resolution NDVI time-series.

differed significantly between sites due to the poor tidal flushing in Tolo Harbour. The levels of Cu, Zn land-locked estuary located in the north-eastern territories of Hong Kong (Fig. 1), is most vulnerable

We engaged in cooperative research with fishers and stakeholders to characterize the fine-scale, spatio-temporal characteristics of spawning behavior in an aggregating marine fish (Cynoscion othonopterus: Sciaenidae) and coincident activities of its commercial fishery in the Upper Gulf of California. Approximately 1.5–1.8 million fish are harvested annually from spawning aggregations of C. othonopterus during 21–25 days of fishing and within an area of 1,149?km2 of a biosphere reserve. Spawning and fishing are synchronized on a semi-lunar cycle, with peaks in both occurring 5 to 2 days before the new and full moon, and fishing intensity and catch are highest at the spawning grounds within a no-take reserve. Results of this study demonstrate the benefits of combining GPS data loggers, fisheries data, biological surveys, and cooperative research with fishers to produce spatio-temporally explicit information relevant to the science and management of fish spawning aggregations and the spatial planning of marine reserves. PMID:22359736

Homeobox genes play crucial roles for the development of multicellular eukaryotes. We have generated a revised list of all homeobox genes for Caenorhabditis elegans and provide a nomenclature for the previously unnamed ones. We show that, out of 103 homeobox genes, 70 are co-orthologous to human homeobox genes. 14 are highly divergent, lacking an obvious ortholog even in other Caenorhabditis species. One of these homeobox genes encodes 12 homeodomains, while three other highly divergent homeobox genes encode a novel type of double homeodomain, termed HOCHOB. To understand how transcription factors regulate cell fate during development, precise spatio-temporal expression data need to be obtained. Using a new imaging framework that we developed, Endrov, we have generated spatio-temporal expression profiles during embryogenesis of over 60 homeobox genes, as well as a number of other developmental control genes using GFP reporters. We used dynamic feedback during recording to automatically adjust the camera exposure time in order to increase the dynamic range beyond the limitations of the camera. We have applied the new framework to examine homeobox gene expression patterns and provide an analysis of these patterns. The methods we developed to analyze and quantify expression data are not only suitable for C. elegans, but can be applied to other model systems or even to tissue culture systems. PMID:26024448

During spontaneous cell polarization of Dictyostelium discoideum cells, phosphatidylinositol (3,4,5)-triphoshpate (PIP3) and PTEN (phosphatase tensin homolog) have been identified as key signaling molecules which govern the process of polarization in a self-organized manner. Recent experiments have quantified the spatio-temporal dynamics of these signaling components. Surprisingly, it was found that membrane-bound PTEN can be either in a high or low state, that PIP3 waves were initiated in areas lacking PTEN through an excitable mechanism, and that PIP3 was degraded even though the PTEN concentration remained low. Here we develop a reaction-diffusion model that aims to explain these experimental findings. Our model contains bistable dynamics for PTEN, excitable dynamics for PIP3, and postulates the existence of two species of PTEN with different dephosphorylation rates. We show that our model is able to produce results that are in good qualitative agreement with the experiments, suggesting that our reaction-diffusion model underlies the self-organized spatio-temporal patterns observed in experiments.

We present SuperFly (http://superfly.crg.eu), a relational database for quantified spatio-temporal expression data of segmentation genes during early development in different species of dipteran insects (flies, midges and mosquitoes). SuperFly has a special focus on emerging non-drosophilid model systems. The database currently includes data of high spatio-temporal resolution for three species: the vinegar fly Drosophila melanogaster, the scuttle fly Megaselia abdita and the moth midge Clogmia albipunctata. At this point, SuperFly covers up to 9 genes and 16 time points per species, with a total of 1823 individual embryos. It provides an intuitive web interface, enabling the user to query and access original embryo images, quantified expression profiles, extracted positions of expression boundaries and integrated datasets, plus metadata and intermediate processing steps. SuperFly is a valuable new resource for the quantitative comparative study of gene expression patterns across dipteran species. Moreover, it provides an interesting test set for systems biologists interested in fitting mathematical gene network models to data. Both of these aspects are essential ingredients for progress toward a more quantitative and mechanistic understanding of developmental evolution. PMID:25404137

The identification of acoustic source accurately is a fundamental problem in noise control. In the practical project, if the contribution of multi-source-noise to the whole was identified, and then the noise level can be reduced accordingly. To get the accurate noise signal, measurements should be possible while the machines are constantly in action. It is easier to get the mixed

Blind audio source separation (BASS) arises in a number of applications in speech and music processing such as speech enhancement, speaker diarization, automated music transcription etc. Generally, BASS methods consider multichannel signal capture. The single microphone case is the most difficult underdetermined case, but it often arises in practice. In the approach considered here, the main source identifiability comes from

The blindsource separation and localization problem for audio signals is studied using microphone arrays. Pure delay mixtures of source signals typically encountered in outdoor environments are considered. Our proposed approach utilizes the subspace methods, including multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithms, to estimate the directions of arrival (DOAs) of the sources from the collected mixtures. Since audio signals are generally considered broadband, the DOA estimates at frequencies with the large sum of squared amplitude values are combined to obtain the final DOA estimates. Using the estimated DOAs, the corresponding mixing and demixing matrices are computed, and the source signals are recovered using the inverse short time Fourier transform. Subspace methods take advantage of the spatial covariance matrix of the collected mixtures to achieve robustness to noise. While the subspace methods have been studied for localizing radio frequency signals, audio signals have their special properties. For instance, they are nonstationary, naturally broadband and analog. All of these make the separation and localization for the audio signals more challenging. Moreover, our algorithm is essentially equivalent to the beamforming technique, which suppresses the signals in unwanted directions and only recovers the signals in the estimated DOAs. Several crucial issues related to our algorithm and their solutions have been discussed, including source number estimation, spatial aliasing, artifact filtering, different ways of mixture generation, and source coordinate estimation using multiple arrays. Additionally, comprehensive simulations and experiments have been conducted to examine various aspects of the algorithm. Unlike the existing blindsource separation and localization methods, which are generally time consuming, our algorithm needs signal mixtures of only a short duration and therefore supports real-time implementation.

It is known from many studies that a large number of micropollutants like pesticides, household products or pharmaceuticals can be found in water bodies. However, there is a general lack of systematic monitoring data that allow for distinguishing between possible sources, detecting temporal trends, or evaluating effects of possible mitigation measures. Including micropollutants in existing monitoring programs is not a trivial task for several reasons (e.g., sorption to sampling equipment, hydrolysis, detection limits etc.). Here, we present systematic concentration and load data for 12 substances (7 pesticides and/or biocides, 3 pharmaceuticals, and 2 anti-corrosives) obtained from a one-year sampling campaign within the "National Long-term Surveillance of Swiss Rivers" (NADUF) programme. Six (partially) nested sampling stations were selected to monitor these compounds in weekly or bi-weekly, flow-proportional samples over one year. Due to the high sensitivity of the LC-MSMS method all compounds could be quantified in almost all samples. Only at the reference site without any effluent from waste water treatment plants and hardly any arable farming, the concentrations were always below the limits of detection of a few ng/L. At all other sites, concentrations generally ranged between 10 and 200 ng/L. Only, the anticorrosive agent benzotriazole often exceeded 1000 ng/L. According to the use of the compounds, different temporal load patterns can be expected. In general, the data confirmed these patterns with almost constant loads of pharmaceuticals at most sites, increased herbicides loads during the periods of agricultural use and positive correlations with discharge year round for biocides used in material protection. However, at some sites the expectations were not met for all compounds. The pain-killer diclofenac for example showed strongly declining loads during the summer months at sites influenced by lake water. This compound is not stable in the epilimnion of lakes, where it has a residence time of several weeks, while it flows through the river system within a few days. This example illustrates how compound properties, season and spatial location may interact and control the occurrence of micropollutants in a stream. The spatial nesting of study catchments made it possible to check the data for plausibility and consistency: we present data on cumulative mass balances downstream and test whether the load development along the river network corresponds to the spatial distribution of possible compound sources (e.g., acreage of arable fields, number of inhabitants etc.). Overall, the data show that monitoring of micropollutants may be achieved even without changing an existing monitoring programme. However, given the generally low concentrations in the composite samples of the NADUF programme compounds with lower use and/or lower stability may fall below the limit of reliable quantification or even detection. A proper interpretation of the data relies on additional (spatio-temporal) information like land use data or precipitation patterns.

In this paper, we consider the problem of blindsource separation in the wavelet domain. We propose a Bayesian estimation framework for the problem where different models of the wavelet coefficients are considered: the independent Gaussian mixture model, the hidden Markov tree model, and the contextual hidden Markov field model. For each of the three models, we give expressions of the posterior laws and propose appropriate Markov chain Monte Carlo algorithms in order to perform unsupervised joint blind separation of the sources and estimation of the mixing matrix and hyper parameters of the problem. Indeed, in order to achieve an efficient joint separation and denoising procedures in the case of high noise level in the data, a slight modification of the exposed models is presented: the Bernoulli-Gaussian mixture model, which is equivalent to a hard thresholding rule in denoising problems. A number of simulations are presented in order to highlight the performances of the aforementioned approach: 1) in both high and low signal-to-noise ratios and 2) comparing the results with respect to the choice of the wavelet basis decomposition. PMID:16830910

Irrigated agricultural production plays a key role in covering the world’s food demand. Its importance will grow in the future given increasing population numbers and uncertain climate. Irrigation, however, has also a major impact on water resources, esp. in the drylands on the planet. For example, most of the large-scale problems of aquifer mining can be linked to groundwater-irrigated agriculture. South Asia is one of these regions of concern where roughly 40 percent of the total global groundwater irrigated area is located. In India, almost half of the total agricultural area is irrigated and it is estimated that groundwater irrigation in the country sustains 27 million ha. Esp. in the northwestern part of the country, water tables are falling at increasing rates that give rise to concern about the future viability of irrigation there. Since the majority of food grains in India are produced in that region, this development is a direct threat to the national food security with potentially global implications. We present a novel remote sensing approach to map the temporal development of irrigated agriculture at large spatial scales with high accuracy. We use time series data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NDVI and surface temperature as well as high-resolution precipitation data from the Indian Meteorological Department from 2000 - 2008 and ancillary data for our supervised classification approach. A cascade of classifiers was chosen to deal with the problem of obtaining labeled examples. A first stage classifier uses large regions of known irrigated and non-irrigated areas to learn a rough estimate of the multi-dimensional time series signature on variables of interest in non-irrigated areas. An estimate of the probability of non-irrigation is generated and passed to a second stage classifier along with the variables used to derive it. The second stage classifier is trained with a small dataset of very high quality estimates of irrigated area, and builds on the output of the first stage to produce a final estimate of the percentage of area irrigated at the pixel scale. The choice of first stage classifier is driven by the need of producing reasonable estimates from large datasets of low-quality data with noise on predictors and labels. The second stage one is chosen with generalization capability in mind, even when small samples are used. We apply our method to northwestern India where our results complement, in a temporal sense, information on irrigation obtained from local census. Due to the linkage of groundwater overdraft and irrigation, the accurate determination of the spatio-temporal evolution of irrigated area coverage allows us to assess the impact of groundwater overdraft and anticipate the consequences of inappropriate water management policies in the region.

Snow cover and its monitoring are important because of the impact on important environmental variables, hydrological circulation and ecosystem services. For regional snow cover mapping and monitoring, the MODIS satellite sensors are particularly appealing. However cloud presence is an important limiting factor. This study addressed the problem of cloud cover for time-series in a boreal-Atlantic region where melting and re-covering of snow often do not follow the usual alpine-like patterns. A key requirement in this context was to apply improved methods to deal with the high cloud cover and the irregular spatio-temporal snow occurrence, through exploitation of space-time correlation of pixel values. The information contained in snow presence sequences was then used to derive summary indices to describe the time series patterns. Finally it was tested whether the derived indices can be considered an accurate summary of the snow presence data by establishing and evaluating their statistical relations with morphology and the landscape. The proposed cloud filling method had a good agreement (between 80 and 99%) with validation data even with a large number of pixels missing. The sequence analysis algorithm proposed takes into account the position of the states to fully consider the temporal dimension, i.e. the order in which a certain state appears in an image sequence compared to its neighbourhoods. The indices that were derived from the sequence of snow presence proved useful for describing the general spatio-temporal patterns of snow in Scotland as they were well related (more than 60% of explained deviance) with environmental information such as morphology supporting their use as a summary of snow patterns over time. The use of the derived indices is an advantage because of data reduction, easier interpretability and capture of sequence position-wise information (e.g. importance of short term fall/melt cycles). The derived seven clusters took into account the temporal patterns of the snow presence and they were well separated both spatially and according to the snow patterns and the environmental information. In conclusion, the use of sequences proved useful for analysing different spatio-temporal patterns of snow that could be related to other environmental information to characterize snow regimes regions in Scotland and to be integrated with ground measures for further hydrological and climatological analysis as baseline data for climate change models.

Virtual representation and simulation of spatio-temporal phenomena is a promising goal for the production of an advanced digital earth. Spread modeling, which is one of the most helpful analyses in the geographic information system (GIS), plays a prominent role in meeting this objective. This study proposes a new model that considers both aspects of static and dynamic behaviors of spreadable

In this paper, we present a novel algorithm for tracking cells in time lapse confocal microscopy movie of a Drosophila epithelial tissue during pupal morphogenesis. We consider a 2D + time video as a 3D static image, where frames are stacked atop each other, and using a spatio-temporal segmentation algorithm we obtain information about spatio-temporal 3D tubes representing evolutions of cells. The main idea for tracking is the usage of two distance functions--first one from the cells in the initial frame and second one from segmented boundaries. We track the cells backwards in time. The first distance function attracts the subsequently constructed cell trajectories to the cells in the initial frame and the second one forces them to be close to centerlines of the segmented tubular structures. This makes our tracking algorithm robust against noise and missing spatio-temporal boundaries. This approach can be generalized to a 3D + time video analysis, where spatio-temporal tubes are 4D objects. PMID:22255854

In this paper, we study a diffusive plant-herbivore system with homogeneous and nonhomogeneous Dirichlet boundary conditions. Stability of spatially homogeneous steady states is established. We also derive conditions ensuring the occurrence of Hopf bifurcation and steady state bifurcation. Interesting transient spatio-temporal behaviors including oscillations in one or both of space and time are observed through numerical simulations. PMID:25974343

The tip-of-the-tongue state (TOT) in face naming is a transient state of difficulty in access to a person's name along with the conviction that the name is known. The aim of the present study was to characterize the spatio-temporal course of brain activation in the successful naming and TOT states, by means of magnetoencephalography, during a…

We present a new tool to enable computationally efficient visualization of data and its spatio-temporal analysis by food safety and public health investigators. Its utility is evaluated in two contexts: (1) Investigation of relationships between cases of Salmonella related human illness and Salmonel...

1 Spatio-temporal variability in benthic silica cycling in two macrotidal1 estuaries: causes of global silica retention in13 estuaries. In this study, the spatial and temporal variability of pore water - was investigated at different15 spatial (meter, longitudinal and cross-section, intra-estuary) and temporal (tidal

Quantifying coupled spatio-temporal dynamics of phenology and hydrology and understanding underlying processes is a fundamental challenge in ecohydrology. While variation in phenology and factors influencing it have attracted the attention of ecologists for a long time, the influence of biodiversity on coupled dynamics of phenology and hydrology across a landscape is largely untested. We measured leaf area index (L) and volumetric soil water content (?) on a co-located spatial grid to characterize forest phenology and hydrology across a forested catchment in central Pennsylvania during 2010. We used hierarchical Bayesian modeling to quantify spatio-temporal patterns of L and ?. Our results suggest that the spatial distribution of tree species across the landscape created unique spatio-temporal patterns of L, which created patterns of water demand reflected in variable soil moisture across space and time. We found a lag of about 11 days between increase in L and decline in ?. Vegetation and soil moisture become increasingly homogenized and coupled from leaf-onset to maturity but heterogeneous and uncoupled from leaf maturity to senescence. Our results provide insight into spatio-temporal coupling between biodiversity and soil hydrology that is useful to enhance ecohydrological modeling in humid temperate forests. PMID:23554915

In biology, more and more information about the interactions in regulatory systems becomes accessible, and this often leads to prior knowledge for recent data interpretations. In this work we focus on multivariate signaling data, where the structure of the data is induced by a known regulatory network. To extract signals of interest we assume a blindsource separation (BSS) model, and we capture the structure of the source signals in terms of a Bayesian network. To keep the parameter space small, we consider stationary signals, and we introduce the new algorithm emGrade, where model parameters and source signals are estimated using expectation maximization. For network data, we find an improved estimation performance compared to other BSS algorithms, and the flexible Bayesian modeling enables us to deal with repeated and missing observation values. The main advantage of our method is the statistically interpretable likelihood, and we can use model selection criteria to determine the (in general unknown) number of source signals or decide between different given networks. In simulations we demonstrate the recovery of the source signals dependent on the graph structure and the dimensionality of the data. PMID:25302766

The goal of this survey was to analyze the spatio-temporal distribution patterns of Culicoides Latreille species (Diptera: Ceratopogonidae) and their relationship with environmental variables in Salta, northwestern Argentina. Culicoides were collected monthly from January 2003 through December 2005. The influence of the climatic variables on population abundance was analyzed with a multilevel Poisson regression. A total of 918 specimens belonging to five species were collected. The most abundant species was Culicoides paraensis Goeldi (65.5%), followed by Culicoides lahillei Iches (14.6%) and Culicoides debilipalpis Lutz (7.6%). The highest seasonal abundance for C. paraensis, C. debilipalpis and C. lahillei occurred during the spring and summer. A Poisson regression analysis showed that the mean maximum and minimum temperature and the mean maximum and minimum humidity were the variables with the greatest influence on the population abundance of Culicoides species. PMID:23461794

We study the control of noise-induced spatio-temporal current density patterns in a semiconductor nanostructure (double barrier resonant tunnelling diode) by multiple time-delayed feedback. We find much more pronounced resonant features of noise-induced oscillations compared to single time feedback, rendering the system more sensitive to variations in the delay time $\\tau$. The coherence of noise-induced oscillations measured by the correlation time exhibits sharp resonances as a function of $\\tau$, and can be strongly increased by optimal choices of $\\tau$. Similarly, the peaks in the power spectral density are sharpened. We provide analytical insight into the control mechanism by relating the correlation times and mean frequencies of noise-induced breathing oscillations to the stability properties of the deterministic stationary current density filaments under the influence of the control loop. Moreover, we demonstrate that the use of multiple time delays enlarges the regime in which the deterministic dynam...

The study analysed monthly satellite RFE (rainfall estimates) from NOAA (National Atmospheric and Oceanic Administration) and monthly rainfall records (January 1996-December 2006) collected from weather stations by NMA (National Meteorological Agency of Ethiopia). Can the RFE data be used reliably to analyse seasonal rainfall variability? After doing spatio-temporal analyses of the two datasets, a significant correlation during the important rainy seasons, summer and spring and a low correlation during winter was shown. In conclusion the RFE images can be used reliably for early warning systems in the country and to empower decision makers on the consequences caused by the changes in the magnitude, timing, duration, and frequency of rainfall deficits on different spatial and temporal scales.

Acousto-optic deflectors (AOD) are promising ultrafast scanners for non-linear microscopy. Their use has been limited until now by their small scanning range and by the spatial and temporal dispersions of the laser beam going through the deflectors. We show that the use of AOD of large aperture (13mm) compared to standard deflectors allows accessing much larger field of view while minimizing spatio-temporal distortions. An acousto-optic modulator (AOM) placed at distance of the AOD is used to compensate spatial and temporal dispersions. Fine tuning of the AOM-AOD setup using a frequency-resolved optical gating (GRENOUILLE) allows elimination of pulse front tilt whereas spatial chirp is minimized thanks to the large aperture AOD. PMID:18607414

Assessment of the cardiac Left Ventricle (LV) wall motion is generally based on visual inspection or quantitative analysis of 2D+t sequences acquired in short-axis cardiac cine-Magnetic Resonance Imaging (MRI). Most often, cardiac dynamic is globally analized from two particular phases of the cardiac cycle. In this paper, we propose an automated method to classify regional wall motion in LV function based on spatio-temporal pro les and Support Vector Machines (SVM). This approach allows to obtain a binary classi cation between normal and abnormal motion, without the need of pre-processing and by exploiting all the images of the cardiac cycle. In each short- axis MRI slice level (basal, median, and apical), the spatio-temporal pro les are extracted from the selection of a subset of diametrical lines crossing opposites LV segments. Initialized at end-diastole phase, the pro les are concatenated with their corresponding projections into the succesive temporal phases of the cardiac cycle. These pro les are associated to di erent types of information that derive from the image (gray levels), Fourier, Wavelet or Curvelet domains. The approach has been tested on a set of 14 abnormal and 6 healthy patients by using a leave-one-out cross validation and two kernel functions for SVM classi er. The best classi cation performance is yielded by using four-level db4 wavelet transform and SVM with a linear kernel. At each slice level the results provided a classi cation rate of 87.14% in apical level, 95.48% in median level and 93.65% in basal level.

The myelin-associated inhibitory proteins (Nogo-A, MAG and OMgp) that prevent axon regeneration in adult CNS, mediate their effects via a receptor referred as NgR1. Beside their inhibitory role in the adult CNS, Nogo-A and NgR1 might also be functionally involved in the developing nervous system. At the present time, no detailed study is available regarding either the onset of NgR1 expression during development or its spatio-temporal pattern of expression relative to the presence of Nogo-A. Two homologs of NgR1, NgR2 and NgR3, have been recently identified, but their function in the nervous system is still unknown in adult as well as during development. We have examined the spatio-temporal expression pattern of both NgR1, NgR2 and NgR3 mRNAs and corresponding proteins in the developing rat olfactory system using in situ hybridization and immunohistochemistry. From E15-E16 onwards, NgR1 mRNA was expressed by differentiating neurons in both the olfactory epithelium and the olfactory bulb. At all developmental stages, including adult animals, NgR1 protein was preferentially targeted to olfactory axons emerging from the olfactory epithelium. Using double-immunostainings in the postnatal olfactory mucosa, we confirm the neuronal localization of NgR1 and its preferential distribution along the olfactory axons. The NgR2 and NgR3 transcripts and their proteins display similar expression profiles in the olfactory system. Together, our data suggest that, in non-pathological conditions, NgR1 and its homologs may play a role in axon outgrowth in the rat olfactory system and may be relevant for the confinement of neural projections within the developing olfactory bulb. PMID:19063867

Human alteration of the patterns of land use/land cover (LULC) on the earths surface is one of the most profound impacts on the functioning of natural ecosystems. At the watershed scale, we expect that not only the amount and type of landscape alteration, but also its spatial distribution and the corresponding watershed characteristics, hydrologic conditions, and biological season will dictate the spatio-temporal patterns of streamwater nitrogen (N). We conducted six synoptic sampling events (50 sites) and weekly streamwater sampling (7 sites) in the West Fork Watershed, a 212 km2 mountainous watershed in Southwestern Montana, which drains the rapidly developing Big Sky resort community. Synoptic sampling campaigns captured each season and a range of hydrological conditions and biologic activity. Samples were analyzed for inorganic and organic forms of nitrogen. We performed exploratory multiple regression analysis to determine the explanatory variables for spatio-temporal streamwater nitrogen (N) patterns. Variables considered included DEM derived hydrologic features (e.g. stream order, upland travel time, slope, aspect, riparian area, riparian buffer ratios, and watershed area), geology, forest cover, and septic locations. These variables were used in generalized least squares with a spatial correlation model based on weighted stream distance to predict streamwater nitrate concentrations. We found greatest correlation (adj r2 =0.90) in the winter with variables including number of septic locations, upland fertilization, and geology. This suggests nitrogen loading to the watershed was more conservatively transported through the uplands and stream network in the winter. In the late summer, however, transport and related biological variables became more important, and included travel time weighted septic loading locations, riparian hillslope buffer ratios, and stream order (adj. r2 =0.21). Here, we present the results of our exploratory statistical analysis as a first step toward modeling the impact of watershed location and spatial distribution of LULC change on the spatial, seasonal, and speciation patterns of streamwater N.

Increasing human pressures and global environmental change may severely affect the diversity of species assemblages and associated ecosystem services. Despite the recent interest in phylogenetic and functional diversity, our knowledge on large spatio-temporal patterns of demersal fish diversity sampled by trawling remains still incomplete, notably in the Mediterranean Sea, one of the most threatened marine regions of the world. We investigated large spatio-temporal diversity patterns by analysing a dataset of 19,886 hauls from 10 to 800 m depth performed annually during the last two decades by standardised scientific bottom trawl field surveys across the Mediterranean Sea, within the MEDITS program. A multi-component (eight diversity indices) and multi-scale (local assemblages, biogeographic regions to basins) approach indicates that only the two most traditional components (species richness and evenness) were sufficient to reflect patterns in taxonomic, phylogenetic or functional richness and divergence. We also put into question the use of widely computed indices that allow comparing directly taxonomic, phylogenetic and functional diversity within a unique mathematical framework. In addition, demersal fish assemblages sampled by trawl do not follow a continuous decreasing longitudinal/latitudinal diversity gradients (spatial effects explained up to 70.6% of deviance in regression tree and generalised linear models), for any of the indices and spatial scales analysed. Indeed, at both local and regional scales species richness was relatively high in the Iberian region, Malta, the Eastern Ionian and Aegean seas, meanwhile the Adriatic Sea and Cyprus showed a relatively low level. In contrast, evenness as well as taxonomic, phylogenetic and functional divergences did not show regional hotspots. All studied diversity components remained stable over the last two decades. Overall, our results highlight the need to use complementary diversity indices through different spatial scales when developing conservation strategies and defining delimitations for protected areas.

Background Epidemics of meningococcal meningitis (MM) recurrently strike the African Meningitis Belt. This study aimed at investigating factors, still poorly understood, that influence annual incidence of MM serogroup A, the main etiologic agent over 2004–2010, at a fine spatial scale in Niger. Methodology/Principal Findings To take into account data dependencies over space and time and control for unobserved confounding factors, we developed an explanatory Bayesian hierarchical model over 2004–2010 at the health centre catchment area (HCCA) level. The multivariate model revealed that both climatic and non-climatic factors were important for explaining spatio-temporal variations in incidence: mean relative humidity during November–June over the study region (posterior mean Incidence Rate Ratio (IRR)?=?0.656, 95% Credible Interval (CI) 0.405–0.949) and occurrence of early rains in March in a HCCA (IRR?=?0.353, 95% CI 0.239–0.502) were protective factors; a higher risk was associated with the percentage of neighbouring HCCAs having at least one MM A case during the same year (IRR?=?2.365, 95% CI 2.078–2.695), the presence of a road crossing the HCCA (IRR?=?1.743, 95% CI 1.173–2.474) and the occurrence of cases before 31 December in a HCCA (IRR?=?6.801, 95% CI 4.004–10.910). At the study region level, higher annual incidence correlated with greater geographic spread and, to a lesser extent, with higher intensity of localized outbreaks. Conclusions Based on these findings, we hypothesize that spatio-temporal variability of MM A incidence between years and HCCAs result from variations in the intensity or duration of the dry season climatic effects on disease risk, and is further impacted by factors of spatial contacts, representing facilitated pathogen transmission. Additional unexplained factors may contribute to the observed incidence patterns and should be further investigated. PMID:24852960

Understanding the influencing factors of the spatio-temporal variability of soil respiration (R (s)) across different ecosystems as well as the evaluation model of R (s) is critical to the accurate prediction of future changes in carbon exchange between ecosystems and the atmosphere. R (s) data from 50 different forest ecosystems in China were summarized and the influences of environmental variables on the spatio-temporal variability of R (s) were analyzed. The results showed that both the mean annual air temperature and precipitation were weakly correlated with annual R (s), but strongly with soil carbon turnover rate. R (s) at a reference temperature of 0°C was only significantly and positively correlated with soil organic carbon (SOC) density at a depth of 20 cm. We tested a global-scale R (s) model which predicted monthly mean R (s) (R (s,monthly)) from air temperature and precipitation. Both the original model and the reparameterized model poorly explained the monthly variability of R (s) and failed to capture the inter-site variability of R (s). However, the residual of R (s,monthly) was strongly correlated with SOC density. Thus, a modified empirical model (TPS model) was proposed, which included SOC density as an additional predictor of R (s). The TPS model explained monthly and inter-site variability of R (s) for 56% and 25%, respectively. Moreover, the simulated annual R (s) of TPS model was significantly correlated with the measured value. The TPS model driven by three variables easy to be obtained provides a new tool for R (s) prediction, although a site-specific calibration is needed for using at a different region. PMID:20571797

In near-Earth space, highly spatio-temporally variant magnetic fields result from solar-terrestrial magnetic interaction. These near-Earth external fields currently represent the largest source of error in efforts to ...

This paper presents a simple yet effective way of improving the estimate of the mixing matrix, in instantaneous blindsource\\u000a separation, by using only reliable data.\\u000a \\u000a The paper describes how the idea of detecting single source data is implemented by selecting only the data which remain for\\u000a two consecutive frames in the same spatial signature. Such data, which are most

Fire changes soil properties directly, through temperature, or indirectly with ash deposition and the temporal elimination of vegetal cover. Both influences change soil colour and soil properties. The degree of changes depends on fire severity that has important implications on soil organic matter, texture, mineralogy and hydrological properties and type of ash produced. The ash colour is different according to the temperature of combustion and burned specie and this property will have implications on soil colour. In addition, ash properties have a strong spatial variability. The aim of this work is to study the spatio-temporal effects of a low severity grassland fire on soil colour occurred in Lithuania, near Vilnius city (54° 42' N, 25° 08' E, 158 m.a.s.l.). After the fire it was designed a plot of 20x20m in a burned and unburned flat area. Soil colour was analysed immediately after the fire, and 2, 5, 7 and 9 months after the fire. In each sampling 25 soil samples were collected, carried out to the laboratory, dried at room temperature (20-24° C) and sieved with the <2mm mesh. Soil colour was observed with the Munsell colour chart and the soil chroma value (CV) was observed. Since data did not respected the Gaussian distribution a neperian logarithmic (ln) transformation was applied. Differences among time and between plots were observed with the repeated measures ANOVA test, followed by a Tukey HSD test. Differences were significant at a p<0.05. The spatial variability (SV) was assessed with the coefficient of variation using non transformed data. The results showed differences among time at a p<0.001, treatment at a p<0.01 and time x treatment at a p<0.01. This means that fire during the first 9 months changed significantly soil colour. The CV of the burned plot was lower than the control plot (darker colour), that is attributed to the deposition of charred material and charcoal. This ash produced in this fire was mainly black coloured. With the time the soil of the burned plot became lighter, due the movement of charred material and charcoal in depth through soil profile. After the fire SV was higher in the burned plot (13.27%) than in the unburned plot (7.95%). This major variability might be attributed to ash influence, since this fire did nit had direct effects on soil. Despite the reduced CV, some patches burned at higher severity, and ash was dark and light grey and this might had influences on soil colour SV. In the following measurements SV was very similar, but always slightly higher in the control plot than in the burned plot. Two months, unburned 15.52% and burned, 14.70%. Five months, unburned, 14.78% and burned 14.42%, Seven months, unburned, 15.15% and burned, 14.67%. Nine months, unburned, 18.96% and burned 17.84%. After the fire ash can be (re)distributed uncountable times. In the immediate period after the fire, finner ash produced at higher severities is easily transported by wind and can remix (Pereira et al., 2013a, Pereira et al., 2013b) and change soil colour. In this fire, vegetation recovered very fast, thus this process might occurred only in the first weeks after the fire (Pereira et al., 2013c). Since vegetation recovered fast, soil colour SV depended on carbon and charred material movement in depth soil profile. Further studies are needed on the soil colour evolution after the fire, since can be an indicator of soil properties such as temperature reached with implications in other soil properties. Acknowledgements The authors appreciated the support of the project "Litfire", Fire effects in Lithuanian soils and ecosystems (MIP-048/2011) funded by the Lithuanian Research Council, Spanish Ministry of Science and Innovation for funding through the HYDFIRE project CGL2010-21670-C02-01, FUEGORED (Spanish Network of Forest Fire Effects on Soils http://grupo.us.es/fuegored/) and to Comissionat per a Universitats i Recerca del DIUE de la Generalitat de Catalunya. References Pereira, P. Cerdà, A., Úbeda, X., Mataix-Solera, J. Arcenegui, V., Zavala, L. (2013a) Mod

Direction of arrival (DOA) estimation is the base of the underwater target orientation and tracking. Based on the average time-delayed correlation matrices, a new kind of plural blindsource separation method using the singular value decomposition is proposed. The array manifold is estimated based on it. Then combining the complex blindsource separation method with the high resolution method, a

In this work we proposed a new method that allows the blindsource separation by the analysis of independent components known as FASTICA in the domain of Wavelet to observe his behavior on signs captured in a real environment. The problem that tries to be solved in BlindSource Separation (BSS) consists of recovering signs statistically independent. Nevertheless, certain difficulties

SIMPLIFIED FORMULATION OF A DEPERMUTATION CRITERION IN CONVOLUTIVE BLINDSOURCE SEPARATION Radoslaw. INTRODUCTION Different methods of independent component analysis (ICA) and blindsource separation (BSS) have {mazur,mertins}@isip.uni-luebeck.de ABSTRACT For the separation of convolutive mixtures, an often used ap

Several algorithms for instantaneous blindsource separation (BSS) have been introduced in the past years. The performance of these algorithms needs to be evaluated and assessed to study their merits and choose the best of them for a given application. In this paper, a new adaptive approach is presented to evaluate different blindsource separation algorithms. In this new approach,

make use of a selection of the output's second- and/or higher-order statistics, the maximumSignal Processing 73 (1999) 1--2 Editorial Blindsource separation and multichannel deconvolution. This special issue concentrates on blindsource separation and multichannel identi- fication and deconvolution

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (IN SUBMISSION) 1 BlindSource Separation compared to those from its surroundings. A system based on SQUIDs (Superconducting Quantum Interference different parts of the brain, we used a blindsource separation (BSS) algorithm to separate the various

During cancer radiotherapy protocols, the early profile of energy deposition is decisive for the prediction and control of radiation-induced biomolecular and sub-cellular damage. A major challenge of spatio-temporal radiation biomedicine, a newly emerging interdisciplinary domain, concerns the complete understanding of biophysical events triggered by an initial energy deposition inside confined ionization clusters (tracks) and evolving over several orders of magnitude, typically from femtosecond (1 fs = 10-15 s) and sub-nanometer scales. The innovating advent of femtosecond laser sources providing ultra-short photon beam and relativistic electron bunches, in the eV and MeV domain respectively, open exciting opportunities for a real-time imaging of radiation-induced biomolecular alterations in nanoscopic tracks. Using a very short-lived quantum probe (2p-like excited electron) and high-time resolved laser spectroscopic methods in the near IR and the temporal window 500 - 5000 fs, we demonstrate that short-range coherent interactions between the quantum probe and a small biosensor of 20 atoms (disulfide molecule) are characterized by an effective reaction radius of 9.6 ± 0.2 angströms. For the first time, femtobioradical investigations performed with aqueous environments give correlated information on spatial and temporal biomolecular damages triggered by a very short lived quantum scalpel whose the gyration radius is around 6 angströms. This innovating approach would be applied to more complex biological architectures such as nucleosomes, healthy and tumour cells. In the framework of high-quality ultra-short penetrating radiation beams devoted to pulsed radiotherapy of cancers, this concept would foreshadow the development of real-time nanobiodosimetry combined to highly-selective targeted pro-drug activation.

of existing algorithms for blindsource separation of convolutive audio mixtures. We provide a taxonomy decades, much attention has been given to the separation of mixed sources, in partic- ular for the blindblindsource separation. In acoustics differ- ent sound sources are recorded simultaneously

As a result of complex human-land interactions and topographic variability, many Mediterranean mountain catchments are covered by agricultural terraces that have locally modified the soil water content dynamic. Understanding these local-scale dynamics helps us grasp better how hydrology behaves on the catchment scale. Thus, this study examined soil water content variability in the upper 30 cm of the soil on a Mediterranean abandoned terrace in north-east Spain. Using a dataset of high spatial (regular grid of 128 automatic TDR probes at 2.5 m intervals) and temporal (20-min time step) resolution, gathered throughout a 84-day period, the spatio-temporal variability of soil water content at the local scale and the way that different spatio-temporal scales reflect the mean soil water content were investigated. Soil water content spatial variability and its relation to wetness conditions were examined, along with the spatial structuring of the soil water content within the terrace. Then, the ability of single probes and of different combinations of spatial measurements (transects and grids) to provide a good estimate of mean soil water content on the terrace scale was explored by means of temporal stability analyses. Finally, the effect of monitoring frequency on the magnitude of detectable daily soil water content variations was studied. Results showed that soil water content spatial variability followed a bimodal pattern of increasing absolute variability with increasing soil water content. In addition, a linear trend of decreasing soil water content as the distance from the inner part of the terrace increased was identified. Once this trend was subtracted, resulting semi-variograms suggested that the spatial resolution examined was too high to appreciate spatial structuring in the data. Thus, the spatial pattern should be considered as random. Of all the spatial designs tested, the 10 × 10 m mesh grid (9 probes) was considered the most suitable option for a good, time-stable estimate of mean soil water content, as no improvement was obtained with the 5 × 5 m mesh grid (30 probes). Finally, the results of temporal aggregation showed that decreasing the monitoring frequency down to 8 h during wetting-up periods and to 1 day during drying-down ones did not result in a loss of information on daily soil water content variations.

Dengue is a peri-urban mosquito-transmitted disease, ubiquitous in the tropics and the subtropics. The geographic distribution of dengue and its more severe form, dengue haemorrhagic fever (DHF), have expanded dramatically in the last decades and dengue is now considered to be the world's most important arboviral disease. Recent demographic changes have greatly contributed to the acceleration and spread of the disease along with uncontrolled urbanization, population growth and increased air travel, which acts as a mechanism for transporting and exchanging dengue viruses between endemic and epidemic populations. The dengue vector and virus are extremely sensitive to environmental conditions such as temperature, humidity and precipitation that influence mosquito biology, abundance and habitat and the virus replication speed. In order to control the spread of dengue and impede epidemics, decision support systems are required that take into account the multi-faceted array of factors that contribute to increased dengue risk. Due to availability of seasonal climate forecasts, that predict the average climate conditions for forthcoming months/seasons in both time and space, there is an opportunity to incorporate precursory climate information in a dengue decision support system to aid epidemic planning months in advance. Furthermore, oceanic indicators from teleconnected areas in the Pacific and Indian Ocean, that can provide some indication of the likely prevailing climate conditions in certain regions, could potentially extend predictive lead time in a dengue early warning system. In this paper we adopt a spatio-temporal Bayesian modelling framework for dengue in Thailand to support public health decision making. Monthly cases of dengue in the 76 provinces of Thailand for the period 1982-2012 are modelled using a multi-layered approach. Explanatory variables at various spatial and temporal resolutions are incorporated into a hierarchical model in order to make spatio-temporal probabilistic predictions of dengue. Potential risk factors considered include altitude, land cover, proximity to road/rail networks and water bodies, temperature and precipitation, oceanic indicators, intervention activities, air traffic volume, population movement, urbanisation and sanitation indicators. In order to quantify unknown or unmeasured dengue risk factors, we use spatio-temporal random effects in the model framework. This helps identify those available indicators which could significantly contribute to a dengue early warning system. We use this model to quantify the extent to which climate indicators can explain variations in dengue risk. This allows us to assess the potential utility of forecast climate information in a dengue decision support system for Thailand. Taking advantage of lead times of several months provided by climate forecasts, public health officials may be able to more efficiently allocate intervention measures, such as targeted vector control activities and provision of medication to deal with more deadly forms of the disease, well ahead of an imminent dengue epidemic.

The results of the application of the linear estimation technique to multiwavelength Raman lidar measurements performed during the summer of 2011 in Greenbelt, MD, USA, are presented. We demonstrate that multiwavelength lidars are capable not only of providing vertical profiles of particle properties but also of revealing the spatio-temporal evolution of aerosol features. The nighttime 3 Beta + 1 alpha lidar measurements on 21 and 22 July were inverted to spatio-temporal distributions of particle microphysical parameters, such as volume, number density, effective radius and the complex refractive index. The particle volume and number density show strong variation during the night, while the effective radius remains approximately constant. The real part of the refractive index demonstrates a slight decreasing tendency in a region of enhanced extinction coefficient. The linear estimation retrievals are stable and provide time series of particle parameters as a function of height at 4 min resolution. AERONET observations are compared with multiwavelength lidar retrievals showing good agreement.

Integration of population data, land-use data, and satellite images can be used to identify and characterize the spatio-temporal extent and expansion trends of urban growth. We provided an idea to investigate the spatio-temporal urban growth using satellite images with population data. We analyze the urban expansion in Japan from 1990 to 2005 by using gridded land-use data, population census data, and DMSP satellite images of nighttime lights. First, we mapped the DMSP nighttime lights and land-use data onto a grid based on the standard 1 km2grid cell system of Japan to determine the proportional areas of DMSP nighttime lights and urban land use within each grid cell. Then, we investigated the relationships among population density, DMSP nighttime lights area, and urban area. A rapid expansion of the urban/built-up area around megacities was associated with population increases; in contrast, population density dropped steeply in rural areas and in small towns. Spatial correlation analysis showed a strong positive correlation between population density and urban land use (r= 0.59). In addition, correlation coefficients between population density and DMSP data increased as the DMSP nighttime lights brightness value increased. We then used census population data as the base population input, and performed a linear multiple regression analysis to predict population density from the combination of urban land-use area and DMSP data in Hokkaido, Japan. Visual and numerical evaluation of the results showed that the combination of urban land-use data and DMSP data could be used to predict the spatial distribution of population density. The results from this study indicated the high correlation between these data and suggested the potentials of population density prediction using DMSP data and land use data. References Bagan, H., and Y. Yamagata. Land-cover change analysis in 50 global cities by using a combination of Landsat data and analysis of grid cell. Environmental Research Letters, vol.9, no. 6, 064015. Jun. 2014. Bagan, H., and Y. Yamagata. Landsat analysis of urban growth: How Tokyo became the world's largest megacity during the last 40 years. Remote Sensing of Environment, vol.127, pp. 210-222. Dec. 2012.

In this paper a mathematical model describing the growth of a solid tumour in the presence of an immune system response is presented. In particular, attention is focused upon the attack of tumour cells by so-called tumour-infiltrating cytotoxic lymphocytes (TICLs), in a small, multicellular tumour, without necrosis and at some stage prior to (tumour-induced) angiogenesis. At this stage the immune cells and the tumour cells are considered to be in a state of dynamic equilibrium--cancer dormancy--a phenomenon which has been observed in primary tumours, micrometastases and residual disease after ablation of the primary tumour. Nonetheless, the precise biochemical and cellular mechanisms by which TICLs control cancer dormancy are still poorly understood from a biological and immunological point of view. Therefore we focus on the analysis of the spatio-temporal dynamics of tumour cells, immune cells and chemokines in an immunogenic tumour. The lymphocytes are assumed to migrate into the growing solid tumour and interact with the tumour cells in such a way that lymphocyte-tumour cell complexes are formed. These complexes result in either the death of the tumour cells (the normal situation) or the inactivation (sometimes even the death) of the lymphocytes. The migration of the TICLs is determined by a combination of random motility and chemotaxis in response to the presence of chemokines. The resulting system of four nonlinear partial differential equations (TICLs, tumour cells, complexes and chemokines) is analysed and numerical simulations are presented. We consider two different tumour geometries--multi-layered cell growth and multi-cellular spheroid growth. The numerical simulations demonstrate the existence of cell distributions that are quasi-stationary in time and heterogeneous in space. A linear stability analysis of the underlying (spatially homogeneous) ordinary differential equation (ODE) kinetics coupled with a numerical investigation of the ODE system reveals the existence of a stable limit cycle. This is verified further when a subsequent bifurcation analysis is undertaken using a numerical continuation package. These results then explain the complex heterogeneous spatio-temporal dynamics observed in the partial differential equation (PDE) system. Our approach may lead to a deeper understanding of the phenomenon of cancer dormancy and may be helpful in the future development of more effective anti-cancer vaccines. PMID:15065736

This report consists of a dissertation submitted to the faculty of the Department of Electrical and Computer Engineering, in partial fulfillment of the requirements for the degree of Doctor of Philosophy, Graduate College, The University of Arizona, 2008. Spatio-temporal systems with heterogeneity in their structure and behavior have two major problems associated with them. The first one is that such complex real world systems extend over very large spatial and temporal domains and consume so many computational resources to simulate that they are infeasible to study with current computational platforms. The second one is that the data available for understanding such systems is limited because they are spread over space and time making it hard to obtain micro and macro measurements. This also makes it difficult to get the data for validation of their constituent processes while simultaneously considering their global behavior. For example, the valley fever fungus considered in this dissertation is spread over a large spatial grid in the arid Southwest and typically needs to be simulated over several decades of time to obtain useful information. It is also hard to get the temperature and moisture data (which are two critical factors on which the survival of the valley fever fungus depends) at every grid point of the spatial domain over the region of study. In order to address the first problem, we develop a method based on the discrete event system specification which exploits the heterogeneity in the activity of the spatio-temporal system and which has been shown to be effective in solving relatively simple partial differential equation systems. The benefit of addressing the first problem is that it now makes it feasible to address the second problem. We address the second problem by making use of a multilevel methodology based on modeling and simulation and systems theory. This methodology helps us in the construction of models with different resolutions (base and lumped models). This allows us to refine an initially constructed lumped model with detailed physics-based process models and assess whether they improve on the original lumped models. For that assessment, we use the concept of experimental frame to delimit where the improvement is needed. This allows us to work with the available data, improve the component models in their own experimental frame and then move them to the overall frame. In this dissertation, we develop a multilevel methodology and apply it to a valley fever model. Moreover, we study the model's behavior in a particular experimental frame of interest, namely the formation of new sporing sites.

The tip-of-the-tongue state (TOT) in face naming is a transient state of difficulty in access to a person's name along with the conviction that the name is known. The aim of the present study was to characterize the spatio-temporal course of brain activation in the successful naming and TOT states, by means of magnetoencephalography, during a face-naming task. Following famous

Based on a previous township-scale model, a spatio-temporal framework is proposed to study the fluctuations of avalanche occurrence\\u000a possibly resulting from climate change. The regional annual component is isolated from the total variability using a two-factor\\u000a nonlinear analysis of variance. Moreover, relying on a Conditional AutoRegressive sub-model for the spatial effects, the structured\\u000a time trend is distinguished from the random

Harmful algal blooms (HABs) pose an enormous threat to the U.S. marine habitation and economy in the coastal waters. Federal and state coastal administrators have been de- vising a state-of-the-art monitoring and forecasting system for these HAB events. The efficacy of a monitoring and forecasting system relies on the performance of HAB detection. We propose a machine learning based spatio-temporal

Spatio-temporal variability in life cycle strategy of four pelagic Antarctic copepods, Rhincalanus gigas, Calanoides acutus, Calanus propinquus and Metridia gerlachei was studied, including their copepodite stage composition, using the multi- year samples taken off east Antarctica (90-160'~) in March 1988-1996. Except for R. gigas, the rare occurrence of adults indicated that the spawning activities ceased by mid-March in this research

Abstract The spatial pattern of Na+ channel clustering in the axon initial segment (AIS) plays a critical role in tuning neuronal computations, and changes in Na+ channel distribution have been shown to mediate novel forms of neuronal plasticity in the axon. However, immunocytochemical data on channel distribution may not directly predict spatio-temporal characteristics of action potential initiation, and prior electrophysiological measures are either indirect (extracellular) or lack sufficient spatial resolution (intracellular) to directly characterize the spike trigger zone (TZ). We took advantage of a critical methodological improvement in the high sensitivity membrane potential imaging (Vm imaging) technique to directly determine the location and length of the spike TZ as defined in functional terms. The results show that in mature axons of mouse cortical layer 5 pyramidal cells, action potentials initiate in a region ?20 ?m in length centred between 20 and 40 ?m from the soma. From this region, the AP depolarizing wave invades initial nodes of Ranvier within a fraction of a millisecond and propagates in a saltatory fashion into axonal collaterals without failure at all physiologically relevant frequencies. We further demonstrate that, in contrast to the saltatory conduction in mature axons, AP propagation is non-saltatory (monotonic) in immature axons prior to myelination. PMID:21669974

Life-history strategies have evolved in response to predictable patterns of environmental features. In practice, linking life-history strategies and changes in environmental conditions requires comparable space-time scales between both processes, a difficult match in most marine system studies. We propose a novel spatio-temporal and dynamic scale to explore marine productivity patterns probably driving reproductive timing in the inshore little penguin (Eudyptula minor), based on monthly data on ocean circulation in the Southern Ocean, Australia. In contrast to what occurred when considering any other fixed scales, little penguin's highly variable laying date always occurred within the annual peak of ocean productivity that emerged from our newly defined dynamic scale. Additionally, local sea surface temperature seems to have triggered the onset of reproduction, acting as an environmental cue informing on marine productivity patterns at our dynamic scale. Chlorophyll-a patterns extracted from this scale revealed that environment factors in marine ecosystems affecting breeding decisions are related to a much wider region than foraging areas that are commonly used in current studies investigating the link between animals' life history and their environment. We suggest that marine productivity patterns may be more predictable than previously thought when environmental and biological data are examined at appropriate scales. PMID:26063848

Using rice (Oryza sativa) as a model crop species, we performed an in-depth temporal transcriptome analysis, covering the early and late stages of Pi deprivation as well as Pi recovery in roots and shoots, using next-generation sequencing. Analyses of 126 paired-end RNA sequencing libraries, spanning nine time points, provided a comprehensive overview of the dynamic responses of rice to Pi stress. Differentially expressed genes were grouped into eight sets based on their responses to Pi starvation and recovery, enabling the complex signaling pathways involved in Pi homeostasis to be untangled. A reference annotation-based transcript assembly was also generated, identifying 438 unannotated loci that were differentially expressed under Pi starvation. Several genes also showed induction of unannotated splice isoforms under Pi starvation. Among these, PHOSPHATE2 (PHO2), a key regulator of Pi homeostasis, displayed a Pi starvation–induced isoform, which was associated with increased translation activity. In addition, microRNA (miRNA) expression profiles after long-term Pi starvation in roots and shoots were assessed, identifying 20 miRNA families that were not previously associated with Pi starvation, such as miR6250. In this article, we present a comprehensive spatio-temporal transcriptome analysis of plant responses to Pi stress, revealing a large number of potential key regulators of Pi homeostasis in plants. PMID:24249833

Urban Heat Island (UHI) refers to the phenomena of higher surface temperature occurring in urban areas as compared to the surrounding countryside attributable to urbanization. Spatio-temporal changes in UHI can be quantified through Land Surface Temperature (LST) derived from satellite imageries. Spatial variations in LST occur due to complexity of land surface - combination of impervious surface materials, vegetation, exposed soils as well as water surfaces. Jaipur city has observed rapid urbanization over the last decade. Due to rising population pressure the city has expanded considerably in areal extent and has also observed substantial land use/land cover (LULC) changes. The paper aims to determine changes in the LST and UHI phenomena for Jaipur city over the period from 2000 to 2011 and analyzes the spatial distribution and temporal variation of LST in context of changes in LULC. Landsat 7 ETM+ (2000) and Landsat 5 TM (2011) images of summer season have been used. Results reveal that Jaipur city has witnessed considerable growth in built up area at the cost of greener patches over the last decade, which has had clear impact on variation in LST. There has been an average rise of 2.99 °C in overall summer temperature. New suburbs of the city record 2° to 4 °C increase in LST. LST change is inversely related to change in vegetation cover and positively related to extent of built up area. The study concludes that UHI of Jaipur city has intensified and extended over new areas.

It is well known that the visual cortex efficiently processes high-dimensional spatial information by using a hierarchical structure. Recently, computational models that were inspired by the spatial hierarchy of the visual cortex have shown remarkable performance in image recognition. Up to now, however, most biological and computational modeling studies have mainly focused on the spatial domain and do not discuss temporal domain processing of the visual cortex. Several studies on the visual cortex and other brain areas associated with motor control support that the brain also uses its hierarchical structure as a processing mechanism for temporal information. Based on the success of previous computational models using spatial hierarchy and temporal hierarchy observed in the brain, the current report introduces a novel neural network model for the recognition of dynamic visual image patterns based solely on the learning of exemplars. This model is characterized by the application of both spatial and temporal constraints on local neural activities, resulting in the self-organization of a spatio-temporal hierarchy necessary for the recognition of complex dynamic visual image patterns. The evaluation with the Weizmann dataset in recognition of a set of prototypical human movement patterns showed that the proposed model is significantly robust in recognizing dynamically occluded visual patterns compared to other baseline models. Furthermore, an evaluation test for the recognition of concatenated sequences of those prototypical movement patterns indicated that the model is endowed with a remarkable capability for the contextual recognition of long-range dynamic visual image patterns. PMID:26147887